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Looking back on the blog

Next week I’ll be starting the second year of my statistics PhD. I’ve learnt a lot from taking the first year classes and studying for the qualifying exams. Some of what I’ve learnt has given me a new perspective on some of my old blog posts. Here are three things that I’ve written about before and I now understand better.

1. The Pi-Lambda Theorem

An early post on this blog was titled “A minimal counterexample in probability theory“. The post was about a theorem from the probability course offered at the Australian National University. The theorem states that if two probability measures agree on a collection of subsets and the collection is closed under finite intersections, then the two probability measures agree on the \sigma-algebra generated by the collection. In my post I give an example which shows that you need the collection to be closed under finite intersections. I also show that you need to have at least four points in the space to find such an example.

What I didn’t know then is that the above theorem is really a corollary of Dykin’s \pi - \lambda theorem. This theorem was proved in my first graduate probability course which was taught by Persi Diaconis. Professor Diaconis kept a running tally of how many times we used the \pi - \lambda theorem in his course and we got up to at least 10. (For more appearances by Professor Diaconis on this blog see here, here and here).

If I were to write the above post again, I would talk about the \pi - \lambda theorem and rename the post “The smallest \lambda-system”. The example given in my post is really about needing at least four points to find a \lambda-system that is not a \sigma-algebra.

2. Mallow’s Cp statistic

The very first blog post I wrote was called “Complexity Penalties in Statistical Learning“. I wasn’t sure if I would write a second and so I didn’t set up a WordPress account. I instead put the post on the website LessWrong. I no longer associate myself with the rationality community but posting to LessWrong was straight forward and helped me reach more people.

The post was inspired in two ways by the 2019 AMSI summer school. First, the content is from the statistical learning course I took at the summer school. Second, at the career fair many employers advised us to work on our writing skills. I don’t know if would have started blogging if not for the AMSI Summer School.

I didn’t know it at the time but the blog post is really about Mallow’s Cp statistic. Mallow’s Cp statistic is an estimate of the test error of a regression model fit using ordinary least squares. The Mallow’s Cp is equal to the training error plus a “complexity penalty” which takes into account the number of parameters. In the blog post I talk about model complexity and over-fitting. I also write down and explain Mallow’s Cp in the special case of polynomial regression.

In the summer school course I took, I don’t remember the name Mallow’s Cp being used but I thought it was a great idea and enjoyed writing about it. The next time encountered Mallow’s Cp was in the linear models course I took last fall. I was delighted to see it again and learn how it fit into a bigger picture. More recently, I read Brad Efron’s paper “How Biased is the Apparent Error Rate of a Prediction Rule?“. The paper introduces the idea of “effective degrees of freedom” and expands on the ideas behind the Cp statistic.

Incidentally, enrolment is now open for the 2023 AMSI Summer School! This summer it will be hosted at the University of Melbourne. I encourage any Australia mathematics or statistics students reading this to take a look and apply. I really enjoyed going in both 2019 and 2020. (Also if you click on the above link you can try to spot me in the group photo of everyone wearing read shirts!)

3. Finitely additive probability measures

In “Finitely additive measures” I talk about how hard it is to define a finitely additive measure on the set of natural numbers that is not countably additive. In particular, I talked about needing to use the Hahn — Banach extension theorem to extend the natural density from the collection of sets with density to the collection of all subsets of the natural numbers.

There were a number of homework problems in my first graduate probability course that relate to this post. We proved that the sets with density are not closed under finite unions and we showed that the square free numbers have density 6/\pi^2.

We also proved that any finite measure defined on an algebra of subsets can be extend to the collection of all subsets. This proof used Zorn’s lemma and the resulting measure is far from unique. The use of Zorn’s lemma relates to the main idea in my blog, that defining an additive probability measure is in some sense non-constructive.

Other posts

Going forward, I hope to continue publishing at least one new post every month. I look forward to one day writing another post like this when I can look back and reflect on how much I have learnt.

Total Variation and Marathon Running

Total variation is a way of measuring how much a function f:[a,b] \to \mathbb{R} “wiggles”. In this post, I want to motivate the definition of total variation by talking about elevation in marathon running.

Comparing marathon courses

On July 24th I ran the 2022 San Francisco (SF) marathon. All marathons are the same distance, 42.2 kilometres (26.2 miles) but individual courses can vary greatly. Some marathons are on road and others are on trails. Some locations can be hot and others can be rainy. And some, such as the SF marathon, can be much hillier than others. Below is a plot comparing the elevation of the Canberra marathon I ran last year to the elevation of the SF marathon:

A plot showing the relative elevation over the course of the Canberra and San Francisco marathons. Try to spot the two times I ran over the Golden Gate Bridge during the SF marathon.

Immediately, you can see that the range of elevation during the San Francisco marathon was much higher than the range of elevation during the Canberra marathon. However, what made the SF marathon hard wasn’t any individual incline but rather the sheer number of ups and downs. For comparison, the plot below shows elevation during a 32 km training run and elevation during the SF marathon:

A plot showing the relative elevation over the course of a training run and the San Francisco marathon. The big climb during my training run is the Stanford dish.

You can see that my training run was mostly flat but had one big hill in the last 10 kilometres. The maximum relative elevation on my training run was about 50 meters higher than the maximum relative elevation of the marathon, but overall the training run graph is a lot less wiggly. This meant there were far more individual hills during the marathon and so the first 32 km of the marathon felt a lot tougher than the training run. By comparing these two runs, you can see that the elevation range can hide important information about the difficulty of a run. We also need to pay attention to how wiggly the elevation curve is.

Wiggliness Scores

So far our definition of wiggliness has been imprecise and has relied on looking at a graph of the elevation. This makes it hard to compare two runs and quickly decide which one is wigglier. It would be convenient if there was a “wiggliness score” – a single number we could assign to each run which measured the wiggliness of the run’s elevation. Bellow we’ll see that total variation does exactly this.

If we zoom in on one of the graphs above, we would see that it actually consists of tiny straight line segments. For example, let’s look at the 22nd kilometre of the SF marathon. In this plot it looks like elevation is a smooth function of distance:

The relative elevation of the 22nd kilometre during the SF marathon.

But if we zoom in on a 100 m stretch, then we see that the graph is actually a series of straight lines glued together:

The relative elevation over a 100 metres during the marathon.

This is because these graphs are made using my GPS watch which makes one recording per second. If we place dots at each of these times, then the straight lines become clearer:

The relative elevation over the same 100 metre stretch. Each blue dots marks a point when a measurement was made.

We can use these blue dots to define the graph’s wiggliness score. The wiggliness score should capture how much the graph varies across its domain. This suggests that wiggliness scores should be additive. By additive, I mean that if we split the domain into a finite number of pieces, then the wiggliness score across the whole domain should be the sum of the wiggliness score of each segment.

In particular, the wiggliness score for the SF marathon is equal to the sum of the wiggliness score of each section between two consecutive blue dots. This means we only need to quantify how much the graph varies between consecutive blue dots. Fortunately, between two such dots, the graph is a straight line. The amount that a straight line varies is simply the distance between the y-value at the start and the y-value at the end. Thus, by adding up all these little distances we can get a wiggliness score for the whole graph. This wiggliness score is used in mathematics, where it is called the total variation.

Here are the wiggliness scores for the three runs shown above:

RunWiggliness score
Canberra Marathon 2021617 m
Training run742 m
SF Marathon 20222140 m
The total variation or wiggliness score for the three graphs shown above.

Total Variation

We’ve seen that by breaking up a run into little pieces, we can calculate the total variation over the course of the run. But how can we calculate the total variation of an arbitrary function f:[a,b] \to \mathbb{R}?

Our previous approach won’t work because the function f might not be made up of straight lines. But we can approximate f with other functions that are made of straight lines. We can calculate the total variation of these approximations using the approach we used for the marathon runs. Then we define the total variation of f as the limit of the total variation of each of these approximations.

To make this precise, we will work with partitions of [a,b]. A partition of [a,b] is a finite set of points P = \{x_0, x_1,\ldots,x_n\} such that:

a = x_0 < x_1 < \ldots < x_n = b.

That is, x_0, x_1,\ldots,x_n is a collection of increasing points in [a,b] that start at a and end at b. For a given partition P of [a,b], we calculate how much the function f varies over the points in the partition P. As with the blue dots above, we can simply add up the distance between consecutive y values f(x_i) and f(x_{i-1}). In symbols, we define V_P(f) (the variation over f over the partition P) to be:

V_P(f) = \sum\limits_{i=1}^n |f(x_i)-f(x_{i-1})|.

To define the variation of f over the interval [a,b], we can imagine taking finer and finer partitions of [a,b]. To do this, note that whenever we add more points to a partition, the total variation over that partition can only increase. Thus, we can think of the total variation of f as the maximum total variation over any partition. We denote the total variation of f by V(f) and define it as:

V(f) = \sup\{V_P(f) : P \text{ is a partition of } [a,b]\}.

Surprisingly, there exist continuous function for which the total variation is infinite. Sample paths of the Brownian motion are canonical examples of continuous functions with infinite total variation. Such functions would be very challenging runs.

Some Limitations

Total variation does a good job of measuring how wiggly a function is but it has some limitations when applied to course elevation. The biggest issue is that total variation treats inclines and declines symmetrically. A steep line sloping down increases the total variation by the same amount as a line with the same slope going upwards. This obviously isn’t true when running; an uphill is very different to a downhill.

To quantify how much a function wiggles upwards, we could use the same ideas but replace the absolute value |f(x_i)-f(x_{i-1})| with the positive part (f(x_i)-f(x_{i-1}))_+ = \max\{f(x_i)-f(x_{i-1}),0\}. This means that only the lines that slope upwards will count towards the wiggliness score. Lines that slope downwards get a wiggliness score of zero.

Another limitation of total variation is that it measures total wiggliness across the whole domain rather than average wiggliness. This isn’t much of a problem when comparing runs of a similar length, but when comparing runs of different lengths, total variation can give surprising results. Below is a comparison between the Australian Alpine Ascent and the SF marathon:

The Australian Alpine Ascent is a 25 km run that goes up Australia’s tallest mountain. Despite the huge climbs during the Australian Alpine Ascent, the SF marathon has a higher total variation. Since the Australian Alpine Ascent was shorter, it gets a lower wiggliness score (1674 m vs 2140 m). For this comparison it would be better to divide each wiggliness score by the runs’ distance.


Despite these limitations, I still think that total variation is a useful metric for comparing two runs. It doesn’t tell you exactly how tough a run will be but if you already know the run’s distance and starting/finishing elevation, then the total variation helps you know what to expect.

Finding Australia’s youngest electorates with R

My partner recently wrote an article for Changing Times, a grassroots newspaper that focuses on social change. Her article, Who’s not voting? Engaging with First Nations voters and young voters, is about voter turn-out in Australia and an important read.

While doing research for the article, she wanted to know which electorates in Australia had the highest proportion of young voters. Fortunately the Australian Electoral Commission (AEC) keeps a detailed record of the number of electors in each electorate available here. The records list the number of voters of a given sex and age bracket in each of Australia’s 153 electorates. To calculate and sort the proportion of young voters (18-29) in each electorate using the most recent records, I wrote the below R code for her:


voters <- read_csv("elector-count-fe-2022.csv", 
                   skip = 8,
                   col_names = c("Description",
                   col_select = (1:14),
                   col_types = cols(Description = "c",
                                    .default = "n"))

not_electorates = c("NSW", "VIC", "ACT", "WA", "SA", "TAS", 
                    "QLD", "NT", "Grand Total","Female", 
                    "Male", "Indeterminate/Unknown")

electorates <- voters %>% 
  filter(!(Description %in% not_electorates),

young_voters <- electorates %>% 
  mutate(Young = `18-19` + `20-24`,
         Proportion = Young/Total,
         rank = min_rank(desc(Proportion))) %>% 
  arrange(rank) %>% 
  select(Electorate = Description, Total, Young, Proportion, rank) 


To explain what I did, I’ll first describe the format of the data set from the AEC. The first column contained a description of each row. All the other columns contained the number of voters in a given age bracket. Rows either corresponded to an electorate or a state and either contained the total electorate or a given sex.

For the article my partner wanted to know which electorate had the highest proportion of young voters (18-24) in total so we removed the rows for each state and the rows that selected a specific sex. The next step was to calculate the number of young voters across the age brackets 18-19 and 20-24 and then calculate the proportion of such voters. Once ranked, the five electorates with the highest proportion of young voters were

ElectorateProportion of young voters

In Who’s not Voting?, my partner points out that the three seats with the highest proportion of voters all swung to the Greens at the most recent election. To view all the proportion of young voters across every electorate, you can download this spreadsheet.

A clock is a one-dimensional subgroup of the torus

Recently, my partner and I installed a clock in our home. The clock previously belonged to my grandparents and we have owned it for a while. We hadn’t put it up earlier because the original clock movement ticked and the sound would disrupt our small studio apartment. After much procrastinating, I bought a new clock movement, replaced the old one and proudly hung up our clock.

Our new clock. We still need to reattach the 5 and 10 which fell off when we moved.

When I first put on the clock hands I made the mistake of not putting them both on at exactly 12 o’clock. This meant that the minute and hour hands were not synchronised. The hands were in an impossible position. At times, the minute hand was at 12 and the hour hand was between 3 and 4. It took some time for me to register my mistake as at some times of the day it can be hard to tell that the hands are out of sync (how often do you look at a clock at 12:00 exactly?). Fortunately, I did notice the mistake and we have a correct clock. Now I can’t help noticing when others make the same mistake such as in this piece of clip art.

An incorrect clock where the minute hand points to 6 but the hour hand is exactly at 10. From 30 Clipart – Clock Face Clip Art

After fixing the clock, I was still thinking about how only some clock hand positions correspond to actual times. This led me to think “a clock is a one-dimensional subgroup of the torus”. Let me explain why.

The torus

The minute and hour hands on a clock can be thought of as two points on two different circles. For instance, if the time is 9:30, then the minute hand corresponds to a point at the very bottom of the circle and the hour hand corresponds to a point 15 degrees clockwise of the leftmost point of the circle. As a clock goes through a 12 hour cycle the minute-hand-point goes around the circle 12 times and the hour-hand-point goes around the circle once. This is shown below.

The blue point goes around its blue circle in time with the minute hand on the clock in the middle. The red point goes around its red circle in time with the hour hand.

If you take the collection of all pairs of points on a circle you get what mathematicians call a torus. The torus is a geometric shape that looks like the surface of a donut. The torus is defined as the Cartesian product of two circles. That is, a single point on the torus corresponds to two points on two different circles. A torus is plotted below.

The green surface above is a torus. The black lines aren’t a part of the torus, they are just there to help the visualisation.

To understand the torus, it’s helpful to consider a more familiar example, the 2-dimensional plane. If we have points x and y on two different lines, then we can produce the point (x,y) in the two dimensional plane. Likewise, if we have a point p and a point q on two different circles, then we can produce a point (p,q) on the torus. Both of these concepts are illustrated below. I have added two circles to the torus which are analogous to the x and y axes of the plane. The blue and red points on the blue and red circle produce the black point on the torus.

Mapping the clock to the torus

The points on the torus are in one-to-one correspondence with possible arrangements of the two clock hands. However, as I learnt putting up our clock, not all arrangements of clock hands correspond to an actual time. This means that only some points on the torus correspond to an actual time but how can we identify these points?

Keeping with our previous convention, let’s use the blue circle to represent the position of the minute hand and the red circle to represent the position of the hour hand. This means that the point where the two circles meet corresponds to 12 o’clock.

The point where the two circles meet corresponds to both hands pointing to 12, that is, 12 o’clock.

There are eleven other points on the red line that correspond to the other times when the minute hand is at 12. That is, there’s a point for 1 o’clock, 2 o’clock, 3 o’clock and so on. Once we add in those points, our torus looks like this:

Each black dot corresponds to when the minute hand is at 12. That is, the dots represent 12 o’clock, 1 o’clock, 2 o’clock and so on.

Finally, we have to join these points together. We know that when the hour hand moves from 12 to 1, the minute hand does one full rotation. This means that we have to join the black points by making one full rotation in the direction of the blue circle. The result is the black curve below that snakes around the torus.

Points on the black curve correspond to actual times on the clock.

The picture above should explain most of this blog’s title – “a clock is a one-dimensional subgroup of the torus”. We now know what the torus is and why certain points on the torus correspond to positions of the hands on a clock. We can see that these “clock points” correspond to a line that snakes around the torus. While the torus is a surface and hence two dimensional, the line is one-dimensional. The last missing part is the word “subgroup”. I won’t go into the details here but the torus has some extra structure that makes it something called a group. Our map from the clock to the torus interacts nicely with this structure and this makes the black line a “subgroup”.

Another perspective

While the above pictures of the torus are pretty, they can be a bit hard to understand and hard to draw. Mathematicians have another perspective of the torus that is often easier to work with. Imagine that you have a square sheet of rubber. If you rolled up the rubber and joined a pair of opposite sides, you would get a rubber tube. If you then bent the tube to join the opposite sides again, you would get a torus! The gif bellow illustrates this idea

By joining opposite sides, we can turn a square into a torus. This gif is taken from

This means that we can simply view the torus as a square. We just have to remember that the opposite sides of the squares have been glued together. So like a game of snake on a phone, if you leave the top of the square, you come out at the same place on the bottom of the square. If we use this idea to redraw our torus it now looks like this:

A drawing of a flat torus. To make a donut shaped torus, the two red lines and then the two blue lines have to be glued together. As before, the blue line corresponds to the minute hand and the red line to the hour hand. When we glue the opposite sides of this square, the four corners all get glued together. This point is where the two circles intersect and corresponds to 12 o’clock.

As before we can draw in the other points when the minute hand is at 12. These points correspond to 1 o’clock, 2 o’clock, 3 o’clock…

Each black dot corresponds to a time when the minute hand is at 12. Remember that each dot on the top is actually the same point as the corresponding dot on the bottom. These opposite points get glued together when we turn the square into a torus.

Finally we can draw in all the other times on the clock. This is the result:

Points on the black line correspond to actual times on the clock. Although it looks like there are 12 different lines, there is actually only one line once we glue the opposite sides together.

One nice thing about this picture is that it can help us answer a classic riddle. In a 12-hour cycle, how many times are the minute and hour hands on top of each other? We can answer this riddle by adding a second line to the above square. The bottom-left to top-right diagonal is the collection of all hand positions where the two hands are on top of each other. Let’s add that line in green and add the points where this new line intersects the black line.

The green line is the collection of all hand positions when the two hands are pointing in the same direction. The black points are where the green and black lines intersect each other.

The points where the green and black lines intersect are hand positions where the clock hands are directly on top of each other and which correspond to actual times. Thus we can count that there are exactly 11 times when the hands are on top of each other in a 12-hour cycle. It might look like there are 12 such times but we have to remember that the corners of the square are all the same point on the torus.

Adding the second hand

So far I have ignored the second hand on the clock. If we included the second hand, we would have three points on three different circles. The corresponding geometric object is a 3-dimensional torus. The 3-dimensional torus is what you get when you take a cube and glue together the three pairs of opposite faces (don’t worry if you have trouble visualising such a shape!).

The points on the 3-dimensional torus which correspond to actual times will again be a line that wraps around the 3-dimensional torus. You could use this line to find out how many times the three hands are all on top of each other! Let me know if you work it out.

I hope that if you’re ever asked to define a clock, you’d at least consider saying “a clock is a one-dimensional subgroup of the torus” and you could even tell them which subgroup!

Sampling from the non-central chi-squared distribution

The non-central chi-squared distribution is a generalisation of the regular chi-squared distribution. The chi-squared distribution turns up in many statistical tests as the (approximate) distribution of a test statistic under the null hypothesis. Under alternative hypotheses, those same statistics often have approximate non-central chi-squared distributions.

This means that the non-central chi-squared distribution is often used to study the power of said statistical tests. In this post I give the definition of the non-central chi-squared distribution, discuss an important invariance property and show how to efficiently sample from this distribution.


Let Z be a normally distributed random vector with mean 0 and covariance I_n. Given a vector \mu \in \mathbb{R}^n, the non-central chi-squared distribution with n degrees of freedom and non-centrality parameter \Vert \mu\Vert_2^2 is the distribution of the quantity

\Vert Z+\mu \Vert_2^2 = \sum\limits_{i=1}^n (Z_i+\mu_i)^2.

This distribution is denoted by \chi^2_n(\Vert \mu \Vert_2^2). As this notation suggests, the distribution of \Vert Z+\mu \Vert_2^2 depends only on \Vert \mu \Vert_2^2, the norm of \mu. The first few times I heard this fact, I had no idea why it would be true (and even found it a little spooky). But, as we will see below, the result is actually a simply consequence of the fact that standard normal vectors are invariant under rotations.

Rotational invariance

Suppose that we have two vectors \mu, \nu \in \mathbb{R}^n such that \Vert \mu\Vert_2^2 = \Vert \nu \Vert_2^2. We wish to show that if Z \sim \mathcal{N}(0,I_n), then

\Vert Z+\mu \Vert_2^2 has the same distribution as \Vert Z + \nu \Vert_2^2.

Since \mu and \nu have the same norm there exists an orthogonal matrix U \in \mathbb{R}^{n \times n} such that U\mu = \nu. Since U is orthogonal and Z \sim \mathcal{N}(0,I_n), we have Z'=UZ \sim \mathcal{N}(U0,UU^T) = \mathcal{N}(0,I_n). Furthermore, since U is orthogonal, U preserves the norm \Vert \cdot \Vert_2^2. This is because, for all x \in \mathbb{R}^n,

\Vert Ux\Vert_2^2 = (Ux)^TUx = x^TU^TUx=x^Tx=\Vert x\Vert_2^2.

Putting all these pieces together we have

\Vert Z+\mu \Vert_2^2 = \Vert U(Z+\mu)\Vert_2^2 = \Vert UZ + U\mu \Vert_2^2 = \Vert Z'+\nu \Vert_2^2.

Since Z and Z' have the same distribution, we can conclude that \Vert Z'+\nu \Vert_2^2 has the same distribution as \Vert Z + \nu \Vert. Since \Vert Z + \mu \Vert_2^2 = \Vert Z'+\nu \Vert_2^2, we are done.


Above we showed that the distribution of the non-central chi-squared distribution, \chi^2_n(\Vert \mu\Vert_2^2) depends only on the norm of the vector \mu. We will now use this to provide an algorithm that can efficiently generate samples from \chi^2_n(\Vert \mu \Vert_2^2).

A naive way to sample from \chi^2_n(\Vert \mu \Vert_2^2) would be to sample n independent standard normal random variables Z_i and then return \sum_{i=1}^n (Z_i+\mu_i)^2. But for large values of n this would be very slow as we have to simulate n auxiliary random variables Z_i for each sample from \chi^2_n(\Vert \mu \Vert_2^2). This approach would not scale well if we needed many samples.

An alternative approach uses the rotation invariance described above. The distribution \chi^2_n(\Vert \mu \Vert_2^2) depends only on \Vert \mu \Vert_2^2 and not directly on \mu. Thus, given \mu, we could instead work with \nu = \Vert \mu \Vert_2 e_1 where e_1 is the vector with a 1 in the first coordinate and 0s in all other coordinates. If we use \nu instead of \mu, we have

\sum\limits_{i=1}^n (Z_i+\nu_i)^2 = (Z_1+\Vert \mu \Vert_2)^2 + \sum\limits_{i=2}^{n}Z_i^2.

The sum \sum_{i=2}^n Z_i^2 follows the regular chi-squared distribution with n-1 degrees of freedom and is independent of Z_1. The regular chi-squared distribution is a special case of the gamma distribution and can be effectively sampled with rejection sampling for large shape parameter (see here).

The shape parameter for \sum_{i=2}^n Z_i^2 is \frac{n-1}{2}, so for large values of n we can efficiently sample a value Y that follows that same distribution as \sum_{i=2}^n Z_i^2 \sim \chi^2_{n-1}. Finally to get a sample from \chi^2_n(\Vert \mu \Vert_2^2) we independently sample Z_1, and then return the sum (Z_1+\Vert \mu\Vert_2)^2 +Y.


In this post, we saw that the rotational invariance of the standard normal distribution gives a similar invariance for the non-central chi-squared distribution.

This invariance allowed us to efficiently sample from the non-central chi-squared distribution. The sampling procedure worked by reducing the problem to sampling from the regular chi-squared distribution.

The same invariance property is also used to calculate the cumulative distribution function and density of the non-central chi-squared distribution. Although the resulting formulas are not for the faint of heart.

Non-measurable sets, cardinality and the axiom of choice

The following post is based on a talk I gave at the 2022 Stanford statistics retreat. The talk was titled “Another non-measurable monster”.

The opening slide from my presentation. The test reads “another non-measurable monster”. There are spooky pumpkins in the background. Image from

The material was based on the discussion and references given in this stackexchange post. The title is a reference to a Halloween lecture on measurability given by Professor Persi Diaconis.

What’s scarier than a non-measurable set?

Making every set measurable. Or rather one particular consequence of making every set measurable.

In my talk, I argued that if you make every set measurable, then there exists a set \Omega and an equivalence relation \sim on \Omega such that |\Omega| < |\Omega / \sim|. That is, the set \Omega has strictly smaller cardinality than the set of equivalence classes \Omega/\sim. The contradictory nature of this statement is illustrated in the picture below

We can think of the set \Omega as the collection of crosses drawn above. The equivalence relation \sim divides \Omega into the regions drawn above. The statement |\Omega|<|\Omega /\sim| means that in some sense there are more regions than crosses.

To make sense of this we’ll first have to be a bit more precise about what we mean by cardinality.

What do we mean by bigger and smaller?

Let A and B be two sets. We say that A and B have the same cardinality and write |A| = |B| if there exists a bijection function f:A \to B. We can think of the function f as a way of pairing each element of A with a unique element of B such that every element of B is paired with an element of A.

We next want to define |A|\le |B| which means A has cardinality at most the cardinality of B. There are two reasonable ways in which we could try to define this relationship

  1. We could say |A|\le |B| means that there exists an injective function f : A \to B.
  2. Alternatively, we could |A|\le |B| means that there exists a surjective function g:B \to A.

Definitions 1 and 2 say similar things and, in the presence of the axiom of choice, they are equivalent. Since we are going to be making every set measurable in this talk, we won’t be assuming the axiom of choice. Definitions 1 and 2 are thus no longer equivalent and we have a decision to make. We will use definition . in this talk. For justification, note that definition 1 implies that there exists a subset B' \subseteq B such that |A|=|B|. We simply take B' to be the range of f. This is a desirable property of the relation |A|\le |B| and it’s not clear how this could be done using definition 2.

Infinite binary sequences

It’s time to introduce the set \Omega and the equivalence relation we will be working with. The set \Omega is the set \{0,1\}^\mathbb{Z} the set of all function \omega : \mathbb{Z} \to \{0,1\}. We can think of each elements \omega \in \Omega as an infinite sequence of zeros and ones stretching off in both directions. For example

\omega = \ldots 1110110100111\ldots.

But this analogy hides something important. Each \omega \in \Omega has a “middle” which is the point \omega_0. For instance, the two sequences below look the same but when we make \omega_0 bold we see that they are different.

\omega = \ldots 111011\mathbf{0}100111\ldots,

\omega' = \ldots 1110110\mathbf{1}00111\ldots.

The equivalence relation \sim on \Omega can be thought of as forgetting the location \omega_0. More formally we have \omega \sim \omega' if and only if there exists n \in \mathbb{Z} such that \omega_{n+k} = \omega_{k}' for all k \in \mathbb{Z}. That is, if we shift the sequence \omega by n we get the sequence \omega'. We will use [\omega] to denote the equivalence class of \omega and \Omega/\sim for the set of all equivalences classes.

Some probability

Associated with the space \Omega are functions X_k : \Omega \to \{0,1\}, one for each integer k \in \mathbb{Z}. These functions simply evaluate \omega at k. That is X_k(\omega)=\omega_k. A probabilist or statistician would think of X_k as reporting the result of one of infinitely many independent coin tosses. Normally to make this formal we would have to first define a \sigma-algebra on \Omega and then define a probability on this \sigma-algebra. Today we’re working in a world where every set is measurable and so don’t have to worry about \sigma-algebras. Indeed we have the following result:

(Solovay, 1970)1 There exists a model of the Zermelo Fraenkel axioms of set theory such that there exists a probability \mathbb{P} defined on all subsets of \Omega such that X_k are i.i.d. \mathrm{Bernoulli}(0.5).

This result is saying that there is world in which, other than the axiom of choice, all the regular axioms of set theory holds. And in this world, we can assign a probability to every subset A \subseteq \Omega in a way so that the events \{X_k=1\} are all independent and have probability 0.5. It’s important to note that this is a true countably additive probability and we can apply all our familiar probability results to \mathbb{P}. We are now ready to state and prove the spooky result claimed at the start of this talk.

Proposition: Given the existence of such a probability \mathbb{P}, |\Omega | < |\Omega /\sim|.

Proof: Let f:\Omega/\sim \to \Omega be any function. To show that |\Omega|<|\Omega /\sim| we need to show that f is not injective. To do this, we’ll first define another function g:\Omega \to \Omega given by g(\omega)=f([\omega]). That is, g first maps \omega to \omega‘s equivalence class and then applies f to this equivalence class. This is illustrated below.

A commutative diagram showing the definition of g as g(\omega)=f([\omega]).

We will show that g : \Omega \to \Omega is almost surely constant with respect to \mathbb{P}. That is, there exists \omega^\star \in \Omega such that \mathbb{P}(g(\omega)=\omega^\star)=1. Each equivalence class [\omega] is finite or countable and thus has probability zero under \mathbb{P}. This means that if g is almost surely constant, then f cannot be injective and must map multiple (in fact infinitely many) equivalence classes to \omega^\star.

It thus remains to show that g:\Omega  \to \Omega is almost surely constant. To do this we will introduce a third function \varphi : \Omega \to \Omega. The map \varphi is simply the shift map and is given by \varphi(\omega)_k = \omega_{k+1}. Note that \omega and \varphi(\omega) are in the same equivalence class for every \omega\in \Omega. Thus, the map g satisfies g\circ \varphi = g. That is g is \varphi-invariant.

The map \varphi is ergodic. This means that if A \subseteq \Omega satisfies \varphi(A)=A, then \mathbb{P}(A) equals 0 or 1. For example if A is the event that 10110 appears at some point in \omega, then \varphi(A)=A and \mathbb{P}(A)=`1. Likewise if A is the event that the relative frequency of heads converges to a number strictly greater than 0.5, then \varphi(A)=A and \mathbb{P}(A)=0. The general claim that all \varphi-invariant events have probability 0 or 1 can be proved using the independence of X_k.

For each k, define an event A_k by A_k = \{\omega : g(\omega)_k = 1\}. Since g is \varphi-invariant we have that \varphi(A_k)=A_k. Thus, \mathbb{P}(A_k)=0 or 1. This gives us a function \omega^\star :\mathbb{Z} \to \{0,1\} given by \omega^\star_k = \mathbb{P}(A_k). Note that for every k, \mathbb{P}(\{\omega : g(\omega)_k = \omega_k^\star\}) = 1. This is because if w_{k}^\star=1, then \mathbb{P}(\{\omega: g(\omega)_k = 1\})=1, by definition of w_k^\star. Likewise if \omega_k^\star =0, then \mathbb{P}(\{\omega:g(\omega)_k=1\})=0 and hence \mathbb{P}(\{\omega:g(\omega)_k=0\})=1. Thus, in both cases, \mathbb{P}(\{\omega : g(\omega)_k = \omega_k^*\})= 1.

Since \mathbb{P} is a probability measure, we can conclude that

\mathbb{P}(\{\omega : g(\omega)=\omega^\star\}) = \mathbb{P}\left(\bigcap_{k \in \mathbb{Z}} \{\omega : g(\omega)_k = \omega_k^\star\}\right)=1.

Thus, g map \Omega to \omega^\star with probability one. Showing that g is almost surely constant and hence that f is not injective. \square

There’s a catch!

So we have proved that there cannot be an injective map f : \Omega/\sim \to \Omega. Does this mean we have proved |\Omega| < |\Omega/\sim|? Technically no. We have proved the negation of |\Omega/\sim|\le |\Omega| which does not imply |\Omega| \le |\Omega/\sim|. To argue that |\Omega| < |\Omega/\sim| we need to produce a map g: \Omega \to \Omega/\sim that is injective. Surprising this is possible and not too difficult. The idea is to find a map g : \Omega \to \Omega such that g(\omega)\sim g(\omega') implies that \omega = \omega'. This can be done by somehow encoding in g(\omega) where the centre of \omega is.

A simpler proof and other examples

Our proof was nice because we explicitly calculated the value \omega^\star where g sent almost all of \Omega. We could have been less explicit and simply noted that the function g:\Omega \to \Omega was measurable with respect to the invariant \sigma-algebra of \varphi and hence almost surely constant by the ergodicity of \varphi.

This quicker proof allows us to generalise our “spooky result” to other sets. Below are two examples where \Omega = [0,1)

  • Fix \theta \in [0,1)\setminus \mathbb{Q} and define \omega \sim \omega' if and only if \omega + n \theta= \omega' for some n \in \mathbb{Z}.
  • \omega \sim \omega' if and only if \omega - \omega' \in \mathbb{Q}.

A similar argument can be used to show that in Solovay’s world |\Omega| < |\Omega/\sim|. The exact same argument follows from the ergodicity of the corresponding actions on \Omega under the uniform measure.

Three takeaways

I hope you agree that this example is good fun and surprising. I’d like to end with some remarks.

  • The first remark is some mathematical context. This argument given today is linked to some interesting mathematics called descriptive set theory. This field studies the properties of well behaved subsets (such as Borel subsets) of topological spaces. Descriptive set theory incorporates logic, topology and ergodic theory. I don’t know much about the field but in Persi’s Halloween talk he said that one “monster” was that few people are interested in the subject.
  • The next remark is a better way to think about our “spooky result”. The result is really saying something about cardinality. When we no longer use the axiom of choice, cardinality becomes a subtle concept. The statement |A|\le |B| no longer corresponds to A being “smaller” than B but rather that A is “less complex” than B. This is perhaps analogous to some statistical models which may be “large” but do not overfit due to subtle constraints on the model complexity.
  • In light of the previous remark, I would invite you to think about whether the example I gave is truly spookier than non-measurable sets. It might seem to you that it is simply a reasonable consequence of removing the axiom of choice and restricting ourselves to functions we could actually write down or understand. I’ll let you decide


  1. Technically Solovay proved that there exists a model of set theory such that every subset of \mathbb{R} is Borel measurable. To get the result for binary sequences we have to restrict to [0,1) and use the binary expansion of x \in [0,1) to define a function [0,1) \to \Omega. Solvay’s paper is available here

The Singular Value Decomposition (SVD)

The singular value decomposition (SVD) is a powerful matrix decomposition. It is used all the time in statistics and numerical linear algebra. The SVD is at the heart of the principal component analysis, it demonstrates what’s going on in ridge regression and it is one way to construct the Moore-Penrose inverse of a matrix. For more SVD love, see the tweets below.

A tweet by Women in Statistics and Data Science about the SVD.
The full thread is here

In this post I’ll define the SVD and prove that it always exists. At the end we’ll look at some pictures to better understand what’s going on.


Let X be a n \times p matrix. We will define the singular value decomposition first in the case n \ge p. The SVD consists of three matrix U \in \mathbb{R}^{n \times p}, \Sigma \in \mathbb{R}^{p \times p} and V \in \mathbb{R}^{p \times p} such that X = U\Sigma V^T. The matrix \Sigma is required to be diagonal with non-negative diagonal entries \sigma_1 \ge \sigma_2 \ge \ldots \ge \sigma_p \ge 0. These numbers are called the singular values of X. The matrices U and V are required to orthogonal matrices so that U^TU=V^TV = I_p, the p \times p identity matrix. Note that since V is square we also have VV^T=I_p however we won’t have UU^T = I_n unless n = p.

In the case when n \le p, we can define the SVD of X in terms of the SVD of X^T. Let \widetilde{U} \in \mathbb{R}^{p \times n}, \widetilde{\Sigma} \in \mathbb{R}^{n \times n} and \widetilde{V} \in \mathbb{R}^{n \times n} be the SVD of X^T so that X^T=\widetilde{U}\widetilde{\Sigma}\widetilde{V}^T. The SVD of X is then given by transposing both sides of this equation giving U = \widetilde{V}, \Sigma = \widetilde{\Sigma}^T=\widetilde{\Sigma} and V = \widetilde{U}.


The SVD of a matrix can be found by iteratively solving an optimisation problem. We will first describe an iterative procedure that produces matrices U \in \mathbb{R}^{n \times p}, \Sigma \in \mathbb{R}^{p \times p} and V \in \mathbb{R}^{p \times p}. We will then verify that U,\Sigma and V satisfy the defining properties of the SVD.

We will construct the matrices U and V one column at a time and we will construct the diagonal matrix \Sigma one entry at a time. To construct the first columns and entries, recall that the matrix X is really a linear function from \mathbb{R}^p to \mathbb{R}^n given by v \mapsto Xv. We can thus define the operator norm of X via

\Vert X \Vert = \sup\left\{ \|Xv\|_2 : \|v\|_2 =1\right\},

where \|v\|_2 represents the Euclidean norm of v \in \mathbb{R}^p and \|Xv\|_2 is the Euclidean norm of Xv \in \mathbb{R}^n. The set of vectors \{v \in \mathbb{R} : \|v\|_2 = 1 \} is a compact set and the function v \mapsto \|Xv\|_2 is continuous. Thus, the supremum used to define \Vert X \Vert is achieved at some vector v_1 \in \mathbb{R}^p. Define \sigma_1 = \|X v_1\|_2. If \sigma_1 \neq 0, then define u_1 = Xv_1/\sigma_1 \in \mathbb{R}^n. If \sigma_1 = 0, then define u_1 to be an arbitrary vector in \mathbb{R}^n with \|u\|_2 = 1. To summarise we have

  • v_1 \in \mathbb{R}^p with \|v_1\|_2 = 1.
  • \sigma_1 = \|X\| = \|Xv_1\|_2.
  • u_1 \in \mathbb{R}^n with \|u_1\|_2=1 and Xv_1 = \sigma_1u_1.

We have now started to fill in our SVD. The number \sigma_1 \ge 0 is the first singular value of X and the vectors v_1 and u_1 will be the first columns of the matrices V and U respectively.

Now suppose that we have found the first k singular values \sigma_1,\ldots,\sigma_k and the first k columns of V and U. If k = p, then we are done. Otherwise we repeat a similar process.

Let v_1,\ldots,v_k and u_1,\ldots,u_k be the first k columns of V and U. The vectors v_1,\ldots,v_k split \mathbb{R}^p into two subspaces. These subspaces are S_1 = \text{span}\{v_1,\ldots,v_k\} and S_2 = S_1^\perp, the orthogonal compliment of S_1. By restricting X to S_2 we get a new linear map X_{|S_2} : S_2 \to \mathbb{R}^n. Like before, the operator norm of X_{|S_2} is defined to be

\|X_{|S_2}\| = \sup\{\|X_{|S_2}v\|_2:v \in S_2, \|v\|_2=1\}.

Since S_2 = \text{span}\{v_1,\ldots,v_k\}^\perp we must have

\|X_{|S_2}\| =  \sup\{\|Xv\|_2:v \in \mathbb{R}^p, \|v\|_2=1, v_j^Tv = 0 \text{ for } j=1,\ldots,k\}.

The set \{v \in \mathbb{R}^p : \|v\|_2=1, v_j^Tv=0\text{ for } j=1,\ldots,k\} is a compact set and thus there exists a vector v_{k+1} such that \|Xv_{k+1}\|_2 = \|X_{|S_2}\|. As before define \sigma_{k+1} = \|Xv_{k+1}\|_2 and u_{k+1} = Xv_{k+1}/\sigma_{k+1} if \sigma_{k+1}\neq 0. If \sigma_{k+1} = 0, then define u_{k+1} to be any vector in \mathbb{R}^{n} that is orthogonal to u_1,u_2,\ldots,u_k.

This process repeats until eventually k = p and we have produced matrices U \in \mathbb{R}^{n \times p}, \Sigma \in \mathbb{R}^{p \times p} and V \in \mathbb{R}^{p \times p}. In the next section, we will argue that these three matrices satisfy the properties of the SVD.


The defining properties of the SVD were given at the start of this post. We will see that most of the properties follow immediately from the construction but one of them requires a bit more analysis. Let U = [u_1,\ldots,u_p], \Sigma = \text{diag}(\sigma_1,\ldots,\sigma_p) and V= [v_1,\ldots,v_p] be the output from the above construction.

First note that by construction v_1,\ldots, v_p are orthogonal since we always had v_{k+1} \in \text{span}\{v_1,\ldots,v_k\}^\perp. It follows that the matrix V is orthogonal and so V^TV=VV^T=I_p.

The matrix \Sigma is diagonal by construction. Furthermore, we have that \sigma_{k+1} \le \sigma_k for every k. This is because both \sigma_k and \sigma_{k+1} were defined as maximum value of \|Xv\|_2 over different subsets of \mathbb{R}^p. The subset for \sigma_k contained the subset for \sigma_{k+1} and thus \sigma_k \ge \sigma_{k+1}.

We’ll next verify that X = U\Sigma V^T. Since V is orthogonal, the vectors v_1,\ldots,v_p form an orthonormal basis for \mathbb{R}^p. It thus suffices to check that Xv_k = U\Sigma V^Tv_k for k = 1,\ldots,p. Again by the orthogonality of V we have that V^Tv_k = e_k, the k^{th} standard basis vector. Thus,

U\Sigma V^Tv_k = U\Sigma e_k = U\sigma_k e_k = \sigma_k u_k.

Above, we used that \Sigma was a diagonal matrix and that u_k is the k^{th} column of U. If \sigma_k \neq 0, then \sigma_k u_k = Xv_k by definition. If \sigma_k =0, then \|Xv_k\|_2=0 and so Xv_k = 0 = \sigma_ku_k also. Thus, in either case, U\Sigma V^Tv_k = Xv_k and so U\Sigma V^T = X.

The last property we need to verify is that U is orthogonal. Note that this isn’t obvious. At each stage of the process, we made sure that v_{k+1} \in \text{span}\{v_1,\ldots,v_k\}^\perp. However, in the case that \sigma_{k+1} \neq 0, we simply defined u_{k+1} = Xv_{k+1}/\sigma_{k+1}. It is not clear why this would imply that u_{k+1} is orthogonal to u_1,\ldots,u_k.

It turns out that a geometric argument is needed to show this. The idea is that if u_{k+1} was not orthogonal to u_j for some j \le k, then v_j couldn’t have been the value that maximises \|Xv\|_2.

Let u_{k} and u_j be two columns of U with j < k and \sigma_j,\sigma_k > 0. We wish to show that u_j^Tu_k = 0. To show this we will use the fact that v_j and v_k are orthonormal and perform “polar-interpolation“. That is, for \lambda \in [0,1], define

v_\lambda = \sqrt{1-\lambda}\cdot v_{j}-\sqrt{\lambda} \cdot v_k.

Since v_{j} and v_k are orthogonal, we have that

\|v_\lambda\|_2^2 = (1-\lambda)\|v_{j}\|_2^2+\lambda\|v_k\|_2^2 = (1-\lambda)+\lambda = 1.

Furthermore v_\lambda is orthogonal to v_1,\ldots,v_{j-1}. Thus, by definition of v_j,

\|Xv_\lambda\|_2^2 \le \sigma_j^2 = \|Xv_j\|_2^2.

By the linearity of X and the definitions of u_j,u_k,

\|Xv_\lambda\|_2^2 = \|\sqrt{1-\lambda}\cdot Xv_j+\sqrt{\lambda}\cdot Xv_{k+1}\|_2^2 = \|\sigma_j\sqrt{1-\lambda}\cdot u_j+\sigma_{k+1}\sqrt{\lambda}\cdot u_{k+1}\|_2^2.

Since Xv_j = \sigma_ju_j and Xv_{k+1}=\sigma_{k+1}u_{k+1}, we have

(1-\lambda)\sigma_j^2 + 2\sqrt{\lambda(1-\lambda)}\cdot \sigma_1\sigma_2 u_j^Tu_{k}+\lambda\sigma_k^2 = \|Xv_\lambda\|_2^2 \le \sigma_j^2

Rearranging and dividing by \sqrt{\lambda} gives,

2\sqrt{1-\lambda}\cdot \sigma_1\sigma_2 u_j^Tu_k \le \sqrt{\lambda}\cdot(\sigma_j^2-\sigma_k^2). for all \lambda \in (0,1]

Taking \lambda \searrow 0 gives u_j^Tu_k \le 0. Performing the same polar interpolation with v_\lambda' = \sqrt{1-\lambda}v_j - \sqrt{\lambda}v_k shows that -u_j^Tu_k \le 0 and hence u_j^Tu_k = 0.

We have thus proved that U is orthogonal. This proof is pretty “slick” but it isn’t very illuminating. To better demonstrate the concept, I made an interactive Desmos graph that you can access here.

This graph shows example vectors u_j, u_k \in \mathbb{R}^2. The vector u_j is fixed at (1,0) and a quarter circle of radius 1 is drawn. Any vectors u that are outside this circle have \|u\|_2 > 1 = \|u_j\|_2.

The vector u_k can be moved around inside this quarter circle. This can be done either cby licking and dragging on the point or changing that values of a and b on the left. The red curve is the path of

\lambda \mapsto \sqrt{1-\lambda}\cdot u_j+\sqrt{\lambda}\cdot u_k.

As \lambda goes from 0 to 1, the path travels from u_j to u_k.

Note that there is a portion of the red curve near u_j that is outside the black circle. This corresponds to a small value of \lambda > 0 that results in \|X v_\lambda\|_2 > \|Xv_j\|_2 contradicting the definition of v_j. By moving the point u_k around in the plot you can see that this always happens unless u_k lies exactly on the y-axis. That is, unless u_k is orthogonal to u_j.

Double cosets and contingency tables

Like my previous post, this blog is also motivated by a comment by Professor Persi Diaconis in his recent Stanford probability seminar. The seminar was about a way of “collapsing” a random walk on a group to a random walk on the set of double cosets. In this post, I’ll first define double cosets and then go over the example Professor Diaconis used to make us probabilists and statisticians more comfortable with all the group theory he was discussing.

Double cosets

Let G be a group and let H and K be two subgroups of G. For each g \in G, the (H,K)-double coset containing g is defined to be the set

HgK = \{hgk : h \in H, k \in K\}

To simplify notation, we will simply write double coset instead of (H,K)-double coset. The double coset of g can also be defined as the equivalence class of g under the relation

g \sim g' \Longleftrightarrow g' = hgk for some h \in H and g \in G

Like regular cosets, the above relation is indeed an equivalence relation. Thus, the group G can be written as a disjoint union of double cosets. The set of all double cosets of G is denoted by H\backslash G / K. That is,

H\backslash G /K = \{HgK : g \in G\}

Note that if we take H = \{e\}, the trivial subgroup, then the double cosets are simply the left cosets of K, G / K. Likewise if K = \{e\}, then the double cosets are the right cosets of H, H \backslash G. Thus, double cosets generalise both left and right cosets.

Double cosets in S_n

Fix a natural number n > 0. A partition of n is a finite sequence a = (a_1,a_2,\ldots, a_I) such that a_1,a_2,\ldots, a_I \in \mathbb{N}, a_1 \ge a_2 \ge \ldots a_I > 0 and \sum_{i=1}^I a_i = n. For each partition of n, a, we can form a subgroup S_a of the symmetric group S_n. The subgroup S_a contains all permutations \sigma \in S_n such that \sigma fixes the sets A_1 = \{1,\ldots, a_1\}, A_2 = \{a_1+1,\ldots, a_1 + a_2\},\ldots, A_I = \{n - a_I +1,\ldots, n\}. Meaning that \sigma(A_i) = A_i for all 1 \le i \le I. Thus, a permutation \sigma \in S_a must individually permute the elements of A_1, the elements of A_2 and so on. This means that, in a natural way,

S_a \cong S_{a_1} \times S_{a_2} \times \ldots \times S_{a_I}

If we have two partitions a = (a_1,a_2,\ldots, a_I) and b = (b_1,b_2,\ldots,b_J), then we can form two subgroups H = S_a and K = S_b and consider the double cosets H \backslash G / K = S_a \backslash S_n / S_b. The claim made in the seminar was that the double cosets are in one-to-one correspondence with I \times J contingency tables with row sums equal to a and column sums equal to b. Before we explain this correspondence and properly define contingency tables, let’s first consider the cases when either H or K is the trivial subgroup.

Left cosets in S_n

Note that if a = (1,1,\ldots,1), then S_a is the trivial subgroup and, as noted above, S_a \backslash S_n / S_b is simply equal to S_n / S_b. We will see that the cosets in S_n/S_b can be described by forgetting something about the permutations in S_n.

We can think of the permutations in S_n as all the ways of drawing without replacement n balls labelled 1,2,\ldots, n. We can think of the partition b = (b_1,b_2,\ldots,b_J) as a colouring of the n balls by J colours. We colour balls 1,2,\ldots, b_1 by the first colour c_1, then we colour b_1+1,b_1+2,\ldots, b_1+b_2 the second colour c_2 and so on until we colour n-b_J+1, n-b_J+2,\ldots, n the final colour c_J. Below is an example when n is equal to 6 and b = (3,2,1).

The first three balls are coloured green, the next two are coloured red and the last ball is coloured blue.

Note that a permutation \sigma \in S_n is in S_b if and only if we draw the balls by colour groups, i.e. we first draw all the balls with colour c_1, then we draw all the balls with colour c_2 and so on. Thus, continuing with the previous example, the permutation \sigma_1 below is in S_b but \sigma_2 is not in S_b.

The permutation \sigma_1 is in S_b because the colours are in their original order but \sigma_1 is not in S_b because the colours are rearranged.

It turns out that we can think of the cosets in S_n \setminus S_b as what happens when we “forget” the labels 1,2,\ldots,n and only remember the colours of the balls. By “forgetting” the labels we mean only paying attention to the list of colours. That is for all \sigma_1,\sigma_2 \in S_n, \sigma_1 S_b = \sigma_2 S_b if and only if the list of colours from the draw \sigma_1 is the same as the list of colours from the draw \sigma_2. Thus, the below two permutations define the same coset of S_b

When we forget the labels and only remember the colours, the permutations \sigma_1 and \sigma_2 look the same and thus are in the same left coset of S_b.

To see why this is true, note that \sigma_1 S_b = \sigma_2 S_b if and only if \sigma_2 = \sigma_1 \circ \tau for some \tau \in S_b. Furthermore, \tau \in S_b if and only if \tau maps each colour group to itself. Recall that function composition is read right to left. Thus, the equation \sigma_2 = \sigma_1 \circ \tau means that if we first relabel the balls according to \tau and then draw the balls according to \sigma_1, then we get the result as just drawing by \sigma_2. That is, \sigma_2 = \sigma_1 \circ \tau for some \tau \in S_b if and only if drawing by \sigma_2 is the same as first relabelling the balls within each colour group and then drawing the balls according to \sigma_1. Thus, \sigma_1 S_b = \sigma_2 S_b, if and only if when we forget the labels of the balls and only look at the colours, \sigma_1 and \sigma_2 give the same list of colours. This is illustrated with our running example below.

If we permute the balls according to \tau \in S_b and the draw according to \sigma_1, then the resulting draw is the same as if we had not permuted and drawn according to \sigma_2. That is, \sigma_2 = \sigma_1 \circ \tau.

Right cosets of S_n

Typically, the subgroup S_a is not a normal subgroup of S_n. This means that the right coset S_a \sigma will not equal the left coset \sigma S_a. Thus, colouring the balls and forgetting the labelling won’t describe the right cosets S_a \backslash S_n. We’ll see that a different type of forgetting can be used to describe S_a \backslash S_n = \{S_a\sigma : \sigma \in S_n\}.

Fix a partition a = (a_1,a_2,\ldots,a_I) and now, instead of considering I colours, think of I different people p_1,p_2,\ldots,p_I. As before, a permutation \sigma \in S_n can be thought of drawing n balls labelled 1,\ldots,n without replacement. We can imagine giving the first a_1 balls drawn to person p_1, then giving the next a_2 balls to the person p_2 and so on until we give the last a_I balls to person p_I. An example with n = 6 and a = (2,2,2) is drawn below.

Person p_1 receives the ball labelled by 6 followed by the ball labelled 3, person p_2 receives ball 2 and then ball 1 and finally person p_3 receives ball 4 followed by ball 5.

Note that \sigma \in S_a if and only if person p_i receives the balls with labels \sum_{k=0}^{i-1}a_k+1,\sum_{k=0}^{i-1}a_k+2,\ldots, \sum_{k=0}^i a_k in any order. Thus, in the below example \sigma_1 \in S_a but \sigma_2 \notin S_a.

When the balls are drawn according to \sigma_1, person p_i receives the balls with labels 2i-1 and 2i, and thus \sigma_1 \in S_a. On the other hand, if the balls are drawn according to \sigma_2, the people receive different balls and thus \sigma_2 \notin S_a.

It turns out the cosets S_a \backslash S_n are exactly determined by “forgetting” the order in which each person p_i received their balls and only remembering which balls they received. Thus, the two permutation below belong to the same coset in S_a \backslash S_n.

When we forget the order in which each person receive their balls, the permutations \sigma_1 and \sigma_2 become the same and thus S_a \sigma_1 = S_a \sigma_2. Note that if we coloured the balls according to the permutation a = (a_1,\ldots,a_I), then we could see that \sigma_1 S_a \neq \sigma_2 S_a.

To see why this is true in general, consider two permutations \sigma_1,\sigma_2. The permutations \sigma_1,\sigma_2 result in each person p_i receiving the same balls if and only if after \sigma_1 we can apply a permutation \tau that fixes each subset A_i  =  \left\{\sum_{k=0}^{i-1}a_k+1,\ldots, \sum_{k=0}^i a_k\right\} and get \sigma_2. That is, \sigma_1 and \sigma_2 result in each person p_i receiving the same balls if and only if \sigma_2 = \tau \circ \sigma_1 for some \tau \in S_a. Thus, \sigma_1,\sigma_2 are the same after forgetting the order in which each person received their balls if and only if S_a \sigma_1 = S_a \sigma_2. This is illustrated below,

If we first draw the balls according to \sigma_1 and then permute the balls according to \tau, then the resulting draw is the same as if we had drawn according to \sigma_2 and not permuted afterwards. That is, \sigma_2 = \tau \circ \sigma_1 .

We can thus see why S_a \backslash S_n \neq S_n / S_a. A left coset \sigma S_a correspond to pre-composing \sigma with elements of S_a and a right cosets S_a\sigma correspond to post-composing \sigma with elements of S_a.

Contingency tables

With the last two sections under our belts, describing the double cosets S_a \backslash S_n / S_b is straight forward. We simply have to combine our two types of forgetting. That is, we first colour the n balls with J colours according to b = (b_1,b_2,\ldots,b_J). We then draw the balls without replace and give the balls to I different people according a = (a_1,a_2,\ldots,a_I). We then forget both the original labels and the order in which each person received their balls. That is, we only remember the number of balls of each colour each person receives. Describing the double cosets by “double forgetting” is illustrated below with a = (2,2,2) and b = (3,2,1).

The permutations \sigma_1 and \sigma_2 both result in person p_1 receiving one green ball and one blue ball. The two permutations also results in p_2 and p_3 both receiving one green ball and one red ball. Thus, \sigma_1 and \sigma_2 are both in the same (S_a,S_b)-double coset. Note however that \sigma_1 S_b \neq \sigma_2 S_b and S_a\sigma_1 \neq S_a \sigma_2.

The proof that double forgetting does indeed describe the double cosets is simply a combination of the two arguments given above. After double forgetting, the number of balls given to each person can be recorded in an I \times J table. The N_{ij} entry of the table is simply the number of balls person p_i receives of colour c_j. Two permutations are the same after double forgetting if and only if they produce the same table. For example, \sigma_1 and \sigma_2 above both produce the following table

Green (c_1) Red (c_2)Blue (c_3)Total
Person p_11012
Person p_21102
Person p_31102

By the definition of how the balls are coloured and distributed to each person we must have for all 1 \le i \le I and 1 \le j \le J

\sum_{j=1}^J N_{ij} = a_i and \sum_{i=1}^I N_{ij} = b_j

An I \times J table with entries N_{ij} \in \{0,1,2,\ldots\} satisfying the above conditions is called a contingency table. Given such a contingency table with entries N_{ij} where the rows sum to a and the columns sum to b, there always exists at least one permutation \sigma such that N_{ij} is the number of balls received by person p_i of colour c_i. We have already seen that two permutations produce the same table if and only if they are in the same double coset. Thus, the double cosets S_a \backslash S_n /S_b are in one-to-one correspondence with such contingency tables.

The hypergeometric distribution

I would like to end this blog post with a little bit of probability and relate the contingency tables above to the hyper geometric distribution. If a = (m, n-m) for some m \in \{0,1,\ldots,n\}, then the contingency tables described above have two rows and are determined by the values N_{11}, N_{12},\ldots, N_{1J} in the first row. The numbers N_{1j} are the number of balls of colour c_j the first person receives. Since the balls are drawn without replacement, this means that if we put the uniform distribution on S_n, then the vector Y=(N_{11}, N_{12},\ldots, N_{1J}) follows the multivariate hypergeometric distribution. Thus, if we have a random walk on S_n that quickly converges to the uniform distribution on S_n, then we could use the double cosets to get a random walk that converges to the multivariate hypergeometric distribution (although there are smarter ways to do such sampling).

The field with one element in a probability seminar

Something very exciting this afternoon. Professor Persi Diaconis was presenting at the Stanford probability seminar and the field with one element made an appearance. The talk was about joint work with Mackenzie Simper and Arun Ram. They had developed a way of “collapsing” a random walk on a group to a random walk on the set of double cosets. As an illustrative example, Persi discussed a random walk on GL_n(\mathbb{F}_q) given by multiplication by a random transvection (a map of the form v \mapsto v + ab^Tv, where a^Tb = 0).

The Bruhat decomposition can be used to match double cosets of GL_n(\mathbb{F}_q) with elements of the symmetric group S_n. So by collapsing the random walk on GL_n(\mathbb{F}_q) we get a random walk on S_n for all prime powers q. As Professor Diaconis said, you can’t stop him from taking q \to 1 and asking what the resulting random walk on S_n is. The answer? Multiplication by a random transposition. As pointed sets are vector spaces over the field with one element and the symmetric groups are the matrix groups, this all fits with what’s expected of the field with one element.

This was just one small part of a very enjoyable seminar. There was plenty of group theory, probability, some general theory and engaging examples.

Update: I have written another post about some of the group theory from the seminar! You can read it here: Double cosets and contingency tables.

Working with ABC Radio’s API in R

This post is about using ABC Radio’s API to create a record of all the songs played on Triple J in a given time period. An API (application programming interface) is a connection between two computer programs. Many websites have API’s that allow users to access data from that website. ABC Radio has an API that lets users access a record of the songs played on the station Triple J since around May 2014. Below I’ll show how to access this information and how to transform it into a format that’s easier to work with. All code can be found on GitHub here.


To access the API in R I used the packages “httr” and “jsonlite”. To transform the data from the API I used the packages “tidyverse” and “lubridate”.


ABC Radio’s API

The URL for the ABC radio’s API is: /api/v1/plays/search.json.

If you type this into a web browser, you can see a bunch of text which actually lists information about the ten most recently played songs on ABC’s different radio stations. To see only the songs played on Triple J, you can go to:

The extra text “station=triplej” is called a parameter. We can add more parameters such as “from” and “to”. The link:

shows information about the songs played in the 30 minutes after midnight UTC on the 16th of January last year. The last parameter that we will use is limit which specifies the number of songs to include. The link:

includes information about the most recently played song. The default for limit is 10 and the largest possible value is 100. We’ll see later that this cut off at 100 makes downloading a lot of songs a little tricky. But first let’s see how we can access the information from ABC Radio’s API in R.

Accessing an API in R

We now know how to use ABC Radio’s API to get information about the songs played on Triple J but how do we use this information in R and how is the information stored. The function GET from the package “httr” enables us to access the API in R. The input to GET is simply the same sort of URL that we’ve been using to view the API online. The below code stores information about the 5 songs played on Triple J just before 5 am on the 5th of May 2015

res <- GET("")
# Response []
# Date: 2022-03-02 23:25
# Status: 200
# Content-Type: application/json
# Size: 62.9 kB

The line “Status: 200” tells us that we have successfully grabbed some data from the API. There are many other status codes that mean different thing but for now we’ll be happy remembering that “Status: 200” means everything has worked.

The information from the API is stored within the object res. To change it from a JSON file to a list we can use the function “fromJSON” from the library jsonlite. This is done below

data <- fromJSON(rawToChar(res$content))
# "total"  "offset" "items"

The information about the individual songs is stored under items. There is a lot of information about each song including song length, copyright information and a link to an image of the album cover. For now, we’ll just try to find the name of the song, the name of the artist and the time the song played. We can find each of these under “items”. Finding the song title and the played time are pretty straight forward:

# "First Light" "Dog"         "Begin Again" "Rot"         "Back To You"
# "2015-05-05T04:55:56+00:00" "2015-05-05T04:52:21+00:00" "2015-05-05T04:47:52+00:00" "2015-05-05T04:41:00+00:00" "2015-05-05T04:38:36+00:00"

Finding the artist name is a bit trickier.

# [[1]]
# entity       arid          name artwork
# 1 Artist maQeOWJQe1 Django Django    NULL
# links
# 1 Link, mlD5AM2R5J,, 4bfce038-b1a0-4bc4-abe1-b679ab900f03, MusicBrainz artist, NA, NA, NA, service, musicbrainz, TRUE
# is_australian    type role
# 1            NA primary   NA
# [[2]]
# entity       arid      name artwork
# 1 Artist maXE59XXz7 Andy Bull    NULL
# links
# 1 Link, mlByoXMP5L,, 3a837db9-0602-4957-8340-05ae82bc39ef, MusicBrainz artist, NA, NA, NA, service, musicbrainz, TRUE
# is_australian    type role
# 1            NA primary   NA
# ....
# ....

We can see that each song actually has a lot of information about each artist but we’re only interested in the artist name. By using “map_chr()” from the library “tidyverse” we can grab each song’s artist name.

map_chr(data$items$recording$artists, ~.$name[1])
# [1] "Django Django" "Andy Bull"     "Purity Ring"   "Northlane"     "Twerps"

Using name[1] means that if there are multiple artists, then we only select the first one. With all of these in place we can create a tibble with the information about these five songs.

list <- data$items
tb <- tibble(
     song_title = list$recording$title,
     artist = map_chr(list$recording$artists, ~.$name[1]),
     played_time = ymd_hms(list$played_time)
) %>% 
# # A tibble: 5 x 3
# song_title  artist        played_time        
# <chr>       <chr>         <dttm>             
# 1 Back To You Twerps        2015-05-05 04:38:36
# 2 Rot         Northlane     2015-05-05 04:41:00
# 3 Begin Again Purity Ring   2015-05-05 04:47:52
# 4 Dog         Andy Bull     2015-05-05 04:52:21
# 5 First Light Django Django 2015-05-05 04:55:56

Downloading lots of songs

The maximum number of songs we can access from the API at one time is 100. This means that if we want to download a lot of songs we’ll need to use some sort of loop. Below is some code which takes in two dates and times in UTC and fetches all the songs played between the two times. The idea is simply to grab all the songs played in a five hour interval and then move on to the next five hour interval. I found including an optional value “progress” useful for the debugging. This code is from the file get_songs.r on my GitHub.

download <- function(from, to){
  base <- ""
  from_char <- format(from, "%Y-%m-%dT%H:%M:%S.000Z")
  to_char <- format(to, "%Y-%m-%dT%H:%M:%S.000Z")
  url <- paste0(base,
  res <- GET(url)
  if (res$status == 200) {
    data <- fromJSON(rawToChar(res$content))
    list <- data$items
    tb <- tibble(
      song_title = list$recording$title,
      artist = map_chr(list$recording$artists, ~.$name[1]),
      played_time = ymd_hms(list$played_time)
    ) %>% 

get_songs <- function(start, end, progress = FALSE){
  from <- ymd_hms(start)
  to <- from + dhours(5)
  end <- ymd_hms(end)
  songs <- NULL
  while (to < end) {
    if (progress) {
    tb <- download(from, to)
    songs <- bind_rows(songs, tb)
    from <- to + dseconds(1)
    to <- to + dhours(5)
  tb <- download(from, end)
  songs <- bind_rows(songs, tb)

An example

Using the functions defined above, we can download all the sounds played in the year 2021 (measured in AEDT).

songs <- get_songs(ymd_hms("2020-12-31 13:00:01 UTC"),
                   ymd_hms("2021-12-31 12:59:59 UTC",
                   progress = TRUE)

This code takes a little while to run so I have saved the output as a .csv file. There are lots of things I hope to do with this data such as looking at the popularity of different songs over time. For example, here’s a plot showing the cumulative plays of Genesis Owusu’s top six songs.

I’ve also looked at using the data from last year to get a list of Triple J’s most played artists. Unfortunately, it doesn’t line up with the official list. The issue is that I’m only recording the primary artist on each song and not any of the secondary artists. Hopefully I’ll address this in a later blog post! I would also like to an analysis of how plays on Triple J correlate with song ranking in the Hottest 100.