[Meta-]Blogging as young researchers

Posted in General, Statistics by Pierre Jacob on 11 December 2014

Summary of the article.

Hello all,

This is an article intended for the ISBA bulletin, jointly written by us all at Statisfaction, Rasmus Bååth from Publishable Stuff, Boris Hejblum from Research side effects, Thiago G. Martins from tgmstat@wordpress, Ewan Cameron from Another Astrostatistics Blog and Gregory Gandenberger from

Inspired by established blogs, such as the popular Statistical Modeling, Causal Inference, and Social Science or Xi’an’s Og, each of us began blogging as a way to diarize our learning adventures, to share bits of R code or LaTeX tips, and to advertise our own papers and projects. Along the way we’ve come to a new appreciation of the world of academic blogging: a never-ending international seminar, attended by renowned scientists and anonymous users alike. Here we share our experiences by weighing the pros and cons of blogging from the point of view of young researchers.


Unfortunate typos in read paper

Posted in General by nicolaschopin on 3 December 2014

Mathieu and I have just realised that the version of our SQMC paper made available on the RSS web site contains several unfortunate typos. In particular, the symbol for “small o” has been replaced by a “big O” by editors. For instance, Theorem 9 should state the QMC beats standard SMC; i.e. the MSE (mean square error) of an SQMC estimator is

\mathrm{MSE} = o(N^{-1})

but in the RSS version, it reads

\mathrm{MSE} = O(N^{-1}).

Well, that’s a bummer. For now, I recommend anyone to read instead the arxiv version (updated on Monday).

SQMC read paper

Posted in General by nicolaschopin on 29 October 2014
the typical RSS meeting

Another way to generate random particles

Almost 10 months since my latest post? I guess bloggin’ ain’t my thing… In my defense, Mathieu Gerber and I were quite busy revising our SQMC paper. I am happy to announce that it has just been accepted as a read paper in JRSSB. If all goes as planned, we should present the paper at  the RSS ordinary meeting on Dec 10. Everybody is welcome to attend, and submit an oral or written discussion (or both). More details soon, when the event is officially announced on the RSS web-site.

What is SQMC? It is a QMC (Quasi-Monte Carlo) version of particle filtering. For the same CPU cost, it typically generates much more accurate estimators. Interested? consider reading the paper here (more recent version coming soon), checking this video where I present SQMC, or, even better, attending our talk in London!


momentify R package at BAYSM14

Posted in General, R, Seminar/Conference, Statistics by Julyan Arbel on 20 September 2014

I presented an arxived paper of my postdoc at the big success Young Bayesian Conference in Vienna. The big picture of the talk is simple: there are situations in Bayesian nonparametrics where you don’t know how to sample from the posterior distribution, but you can only compute posterior expectations (so-called marginal methods). So e.g. you cannot provide credible intervals. But sometimes all the moments of the posterior distribution are available as posterior expectations. So morally, you should be able to say more about the posterior distribution than just reporting the posterior mean. To be more specific, we consider a hazard (h) mixture model

\displaystyle h(t)=\int k(t;y)\mu(dy)

where k is a kernel, and the mixing distribution \mu is random and discrete (Bayesian nonparametric approach).

We consider the survival function S which is recovered from the hazard rate h by the transform

\displaystyle S(t)=\exp\Big(-\int_0^t h(s)ds\Big)

and some possibly censored survival data having survival S. Then it turns out that all the posterior moments of the survival curve S(t) evaluated at any time t can be computed.

The nice trick of the paper is to use the representation of a distribution in a [Jacobi polynomial] basis where the coefficients are linear combinations of the moments. So one can sample from [an approximation of] the posterior, and with a posterior sample we can do everything! Including credible intervals.

I’ve wrapped up the few lines of code in an R package called momentify (not on CRAN). With a sequence of moments of a random variable supported on [0,1] as an input, the package does two things:

  • evaluates the approximate density
  • samples from it

A package example for a mixture of beta and 2 to 7 moments gives that result:


Non-negative unbiased estimators

Posted in Statistics by Pierre Jacob on 13 May 2014
Benedict Cumberbatch having to choose between unbiasedness and non-negativity.

Benedict has to choose between unbiasedness and non-negativity.

Hey hey,

With Alexandre Thiéry we’ve been working on non-negative unbiased estimators for a while now. Since I’ve been talking about it at conferences and since we’ve just arXived the second version of the article, it’s time for a blog post. This post is kind of a follow-up of a previous post from July, where I was commenting on Playing Russian Roulette with Intractable Likelihoods by Mark Girolami, Anne-Marie Lyne, Heiko Strathmann, Daniel Simpson, Yves Atchade.


Parallel resampling in the particle filter

Posted in Statistics by Pierre Jacob on 12 May 2014
Decisions decisions...

Decisions decisions… which resampling to use on the CPU (left), GPU (middle), or between both (right). The y-axis essentially parametrizes the expected variance of the weights (high values = “we observe an outlier” = high variance of the weights).

Hey there,

It’s been a while I haven’t written about parallelization and GPUs. With colleagues Lawrence Murray and Anthony Lee we have just arXived a new version of Parallel resampling in the particle filter. The setting is that, on modern computing architectures such as GPUs, thousands of operations can be performed in parallel (i.e. simultaneously) and therefore the rest of the calculations that cannot be parallelized quickly becomes the bottleneck. In the case of the particle filter (or any sequential Monte Carlo method such as SMC samplers), that bottleneck is the resampling step. The article investigates this issue and numerically compares different resampling schemes.


Moustache target distribution and Wes Anderson

Posted in Art, Geek, R by Pierre Jacob on 31 March 2014

Today I am going to introduce the moustache target distribution (moustarget distribution for brievety). Load some packages first.

library(wesanderson) # on CRAN
library(RShapeTarget) # available on
library(PAWL) # on CRAN

Let’s invoke the moustarget distribution.

 shape <- create_target_from_shape(
file_name=system.file(package = "RShapeTarget", "extdata/moustache.svg"),
rinit <- function(size) matrix(rnorm(2*size), ncol = 2)
moustarget <- target(name = "moustache", dimension = 2,
rinit = rinit, logdensity = shape$logd,
parameters = shape$algo_parameters)

This defines a target distribution represented by a SVG file using RShapeTarget. The target probability density function is defined on \mathbb{R}^2 and is proportional to 1 on the segments described in the SVG files, and decreases exponentially fast to 0 away from the segments. The density function of the moustarget is plotted below, a picture being worth a thousand words.


Beautiful Science: Picturing Data, Inspiring Insight

Posted in General by Pierre Jacob on 19 March 2014

Martin Krzywinski, Circles of Life, 2013 (© Martin Krzywinski)


There’s a nice exhibition open until May 26th at the British Library in London, entitled Beautiful Science: Picturing Data, Inspiring Insight. Various examples of data visualizations are shown, either historical or very modern, or even made especially for the exhibition. Definitely worth a detour if you happen to be in the area, you can see everything in 15 minutes.

In particular there are nice visualisations of historical climate data, gathered from the logbooks of the English East India company, whose ships were crossing every possible sea in the beginning of the 19th century. The logbooks contain locations and daily weather reports, handwritten by the captains themselves. Turns out the logbooks are kept at the British Library itself and some of them are on display at the exhibition. More info on that project here:

Rasmus Bååth’s Bayesian first aid

Posted in Project, R, Statistics by Pierre Jacob on 23 January 2014

Besides having coded a pretty cool MCMC app in Javascript, this guy Rasmus Bååth has started the Bayesian first aid project. The idea is that if there’s an R function called blabla.test performing test “blabla”, there should be a function bayes.blabla.test performing a similar test in a Bayesian framework, and showing the output in a similar way so that the user can easily compare both approaches.This post explains it all. Jags and BEST seem to be the two main workhorses under the hood.

Kudos to Rasmus for this very practical approach, potentially very impactful. Maybe someday people will have to specify if they want a frequentist approach and not the other way around! (I had a dream, etc).

Quantitative arguments as hypermedia

Posted in General by jodureau on 16 January 2014

Hey readers,

I’m Joseph Dureau, I have been an avid reader of this blog for while now, and I’m very glad Pierre proposed me to share a few things. Until a few months ago, I used to work on Bayesian inference methods for stochastic processes, with applications to epidemiology. Along with fellow colleagues from this past life, I have now taken the startup path, founding Standard Analytics. We’re looking into how web technologies can be used to enhance browsability, transparency and impact of scientific publications. Here’s a start on what we’ve been up to so far.
Let me just make it clear that everything I’m presenting is fully open source, and available here. I hope you’ll find it interesting, and we’re very excited to hear from you! Here it goes..

To date, the Web has developed most rapidly as a medium of documents for people  rather than for data and information that can be processed automatically.
Berners-Lee et al, 2001

Since this sentence was written, twelve years ago, ambitious and collective initiatives have been undertaken to revolutionize what machines can do for us on the web. When I make a purchase online, my email service is able to understand it from the purchase confirmation email, communicate to the online store service, authenticate, obtain information on the delivery, and provide me with a real-time representation of where the item is located. Machines now have the means to process data in a smarter way, and to communicate over it!

However, when it comes to exchanging quantitative arguments, be it in a blog post or in a scientific article, web technology does not bring us much further than what can be done with pen and paper. (more…)

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