## Sub-Gaussian property for the Beta distribution (part 3, final)

In this third and last post about the Sub-Gaussian property for the Beta distribution [1] (post 1 and post 2), I would like to show the interplay with the Bernoulli distribution as well as some connexions with optimal transport (OT is a hot topic in general, and also on this blog with Pierre’s posts on Wasserstein ABC). (more…)

## Sub-Gaussian property for the Beta distribution (part 2)

As a follow-up on my previous post on the sub-Gaussian property for the Beta distribution [1], I’ll give here a visual illustration of the proof.

A random variable with finite mean is sub-Gaussian if there is a positive number such that:

We focus on *X* being a Beta random variable. Its moment generating function is known as the Kummer function, or confluent hypergeometric function . So *X *is -sub-Gaussian as soon as the difference function

remains positive on . This difference function is plotted on the right panel above for parameters . In the plot, is varying from green for the variance (which is a lower bound to the optimal proxy variance) to blue for the value , a simple upper bound given by Elder (2016), [2]. The idea of the proof is simple: the optimal proxy-variance corresponds to the value of for which admits a double zero, as illustrated with the red curve (black dot). The left panel shows the curves with varying, interpolating from green for to blue for , with only one curve qualifying as the optimal proxy variance in red.

#### References

[1] Marchal and Arbel (2017), On the sub-Gaussianity of the Beta and Dirichlet distributions. Electronic Communications in Probability, 22:1–14, 2017. Code on GitHub.

[2] Elder (2016), Bayesian Adaptive Data Analysis Guarantees from Subgaussianity, https://arxiv.org/abs/1611.00065

## A big problem in our community

Hi all,

Kristian Lum, who was already one of my Statistics superheroes for her many interesting papers and great talks, bravely wrote the following text about her experience as a young statistician going to conferences:

https://medium.com/@kristianlum/statistics-we-have-a-problem-304638dc5de5

I can’t thank Kristian enough for speaking out. Her experience is both shocking and hardly surprising. Many, many academics report similar stories. This simply can’t go on like that.

I happen to have gone to the conferences mentioned by Kristian, and my experience as a young man was completely different. It was all about meeting interesting people, discussing ideas, being challenged, and having good times. Nobody harassed, touched or assaulted me. There was some flirting, as I guess is natural when hundreds of people are put in sunny places far away from home, but I was never the victim of any misconduct or abuse of power. So instead of driving me out of the field, conferences became important, enriching and rewarding moments of my professional life.

Looking back at those conferences I feel sick, and heartbroken, at the thought that some of my peers were having such a difficult time, because of predators who don’t ever face the consequences of their actions. Meanwhile I was part of the silent majority.

The recent series of revelations about sexual harassment and assaults in other professional environments indicate that this is not specific to our field, nor to academia. But this does not make it any more acceptable. I know for a fact that many leaders of our field take this issue extremely seriously (as Kristian mentions too), but clearly much much more needs to be done. The current situation is just shameful; strong and coordinated actions will be needed to fix it. Thanks again to Kristian for the wake-up call.

## nrow, references and copies

Hi all,

This post deals with a strange phenomenon in R that I have noticed while working on unbiased MCMC. Reducing the problem to a simple form, consider the following code, which iteratively samples a vector ‘x’ and stores it in a row of a large matrix called ‘chain’ (I’ve kept the MCMC terminology).

dimstate = 100 nmcmc = 1e4 chain = matrix(0, nrow = nmcmc, ncol = dimstate) for (imcmc in 1:nmcmc){ if (imcmc == nrow(chain)){ #call to nrow } x = rnorm(dimstate, mean = 0, sd = 1) chain[imcmc,] = x #copying of x in chain }

If you execute this code, you will see that it is surprisingly slow: it takes close to a minute on my computer. Now, consider the next block, which does exactly the same except that the vector ‘x’ is not copied into the matrix ‘chain’.

dimstate = 100 nmcmc = 1e4 chain = matrix(0, nrow = nmcmc, ncol = dimstate) for (imcmc in 1:nmcmc){ if (imcmc == nrow(chain)){ #call to nrow } x = rnorm(dimstate, mean = 0, sd = 1) # chain[imcmc,] = x #no more copying }

This code runs nearly instantaneously. Could it be that just copying a vector in a matrix takes a lot of time? Sounds unlikely. Now consider this third block.

dimstate = 100 nmcmc = 1e4 chain = matrix(0, nrow = nmcmc, ncol = dimstate) for (imcmc in 1:nmcmc){ if (imcmc == nmcmc){ #no call to nrow } x = rnorm(dimstate, mean = 0, sd = 1) chain[imcmc,] = x #copying of x in chain }

This code runs nearly instantaneously as well; this time ‘x’ is copied into ‘chain’, but the call to the nrow function is removed….?! What is nrow doing? It is meant to simply return dim(chain)[1], the first dimension of chain. So consider this fourth block.

dimstate = 100 nmcmc = 1e4 chain = matrix(0, nrow = nmcmc, ncol = dimstate) for (imcmc in 1:nmcmc){ if (imcmc == dim(chain)[1]){ #call to dim instead of nrow } x = rnorm(dimstate, mean = 0, sd = 1) chain[imcmc,] = x #copying of x in chain }

This one also runs instantaneously! So replacing nrow(chain) by dim(chain)[1] solves the problem. Why?

The answer comes from R guru and terrific statistician Louis Aslett. I directly quote from an exchange of emails, since he brilliantly explains the phenomenon.

You probably know R stores everything by reference, so if I do:

x <- matrix(0, nrow=1e5, ncol=100)

y <- xI actually only have one copy of the matrix in memory with two references to it. If I then do:

x[1,1] <- 1

R will first make a copy of the whole matrix, update x to point to that and then change the first element to one. This idea is used when you pass a variable to a standard (i.e. non-core, non-primitive) R function, which nrow is: it creates a reference to the variable you pass so that it doesn’t have to copy and the function call is very fast …. as long as you don’t write to it inside the function, no copy need ever happen. But the “bad design” bit is that R makes a decision whether to copy on write based only on a reference count and crucially that reference count stays increased even after a function returns, irrespective of whether or not the function has touched the variable.

So:

x <- matrix(0, nrow=1e5, ncol=100) # matrix has ref count 1

x[1,1] <- 1 # ref count is 1, so write with no copy

nrow(x) # ref count is 2 even though nothing was touched

x[1,1] <- 1 # ref count still 2, so R copies before writing first element. Now the ref count drops to 1 again

x[2,2] <- 1 # this writes without a copy as ref count got reset on last line

nrow(x) # ref count jumps

x[3,3] <- 1 # copy invoked again! Aaaargh!So by calling nrow in the loop for the first example, the chain matrix is being copied in full on every iteration. In the second example, chain is never written to so there is no negative side effect to the ref count having gone up. In the third example, chain only ever has ref count 1 so there are no copies and each row is written in-place. I did a quick bit of profiling and indeed in the slow example, the R garbage collector allocates and tidies up nearly 9GB of RAM when executing the loop!

The crazy thing is that dim(chain)[1] works full speed even though that is all that nrow is doing under the hood, but the reason is that dim is a so-called “primitive” core R function which is special because it doesn’t affect the reference counter of its arguments. If you want to dig into this yourself, there’s a function refs() in the pryr package which tells you the current reference count to any variable.

Thanks Louis!

## Bayesian model comparison with vague or improper priors

Hi,

With Stephane Shao, Jie Ding and Vahid Tarokh we have just arXived a tech report entitled “Bayesian model comparison with the Hyvärinen score: computation and consistency“. Here I’ll explain the context, that is, scoring rules and Hyvärinen scores (originating in Hyvärinen’s score matching approach to inference), and then what we actually do in the paper.

## Dynamic publication list for research webpage using arXiv, HAL, or bibtex2html

Well of course, dynamic is conditional upon some manual feeding. If you put your papers on arXiv or HAL, then those two propose dynamic widgets. If you maintain a .bib file of your papers, you can use tools like bibtex2html. This is not dynamic at all, but it allows for finer tuning of url links you might want to add than with arXiv or HAL options. I review below those three options. (more…)

## ISBA elections, let’s go voting

The International Society for Bayesian Analysis (ISBA), is running elections until November, 15. This year, two contributors on this blog, Nicolas Chopin and myself, are running for an ISBA Section office. The sections of the society, nine in number as of today, gather researchers with common research interests: Computation, Objective Bayes, Nonparametrics, etc.

Here are our candidate statements:

## Approximating the cut distribution

Hi,

This post is about computational issues with the cut distribution for Bayesian inference in misspecified models. Some motivation was given in a previous post about a recent paper on modular Bayesian inference. The cut distribution, or variants of it, might play an important role in combining statistical models, especially in settings where one wants to propagate uncertainty while preventing misspecification from damaging estimation. The cut distribution can also be seen as a probabilistic analog of two-step point estimators. So the cut distribution is more than just a trick! And it raises interesting computational issues which I’ll describe here along with a solution via unbiased MCMC.

## Unbiased Hamiltonian Monte Carlo with couplings

With Jeremy Heng we have recently arXived a paper describing how to remove the burn-in bias of Hamiltonian Monte Carlo (HMC). This follows a recent work on unbiased MCMC estimators in general on which I blogged here. The case of HMC requires a specific yet very simple coupling. A direct consequence of this work is that Hamiltonian Monte Carlo can be massively parallelized: instead of running one chain for many iterations, one can run short coupled chains independently in parallel. The proposed estimators are consistent in the limit of the number of parallel replicates. This is appealing as the number of available processors increases much faster than clock speed, over recent years and for the years to come, for a number of reasons explained e.g. here.

## New R user community in Grenoble, France

Nine R user communities already exist in France and there is a much large number of R communities around the world. It was time for Grenoble to start its own!

The goal of the R user group is to facilitate the identification of local useRs, to initiate contacts, and to organise experience and knowledge sharing sessions. The group is open to any local useR interested in learning and sharing knowledge about R.

The group’s website features a map and table with members of the R group. Members with specific skills related to the use of R are referenced in a table and can be contacted by other members. A gitter allows members to discuss R issues and a calendar presents the upcoming events. (more…)

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