# Statisfaction

## 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 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… 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 https://github.com/pierrejacob/RShapeTarget/
library(PAWL) # on CRAN


Let’s invoke the moustarget distribution.

 shape <- create_target_from_shape(
file_name=system.file(package = "RShapeTarget", "extdata/moustache.svg"),
lambda=5)
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

Hey,

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: oldweather.org.

## 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

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…)

Tagged with: , , ,

## MCM’Ski lessons

Posted in Seminar/Conference by Pierre Jacob on 16 January 2014

A few days after the MCMSki conference, I start to see the main lessons gathered there.

1. I should really read the full program before attending the next MCMSki. The three parallel sessions looked consistently interesting, and I really regret having missed some talks (in particular Dawn Woodard‘s and Natesh Pillai‘s) and some posters as well (admittedly, due to exhaustion on my part).
2. Compared to the previous instance three years ago (in Utah), the main themes have significantly changed. Scalability, approximate methods, non-asymptotic results, 1/n methods … these keywords are now on everyone’s lips. Can’t wait to see if MCQMC’14 will feel that different from MCQMC’12.
3. The community is rightfully concerned about scaling Monte Carlo methods to big data, with some people pointing out that models should also be rethought in this new context.
4. The place of software developers in the conference, or simply references to software packages in the talks, is much greater than it used to be. It’s a very good sign towards reproducible research in our field. There’s still a lot of work to do, in particular in terms of making parallel computing easier to access (time to advertise LibBi a little bit). On a related note, many people now point out whether their proposed algorithms are parallel-friendly or not.
5. Going from the Rockies to the Alps, the food drastically changed from cheeseburgers to just melted cheese. Bread could be found but ground beef and Budweiser were reported missing.
6. It’s fun to have an international conference in your home country, but switching from French to English all the time was confusing.

Back in flooded Oxford now!

## See you in Chamonix

Posted in General by nicolaschopin on 3 January 2014

Happy new year to everyone, and perhaps see you at MCMski 4 in Chamonix next week, which I expect to be a very friendly and exciting even if I’m not much into skiing. :-)

I will talk for the first time about SQMC, a QMC (Quasi Monte Carlo) variant of particle filtering (PF) that Mathieu Gerber and I developed in the recent months. We are quite excited about it for a variety of reasons, but I will give more details shortly on this blog.

I thought that my talk would clash with a session on PMCMC, which was quite unfortunate as I suspect that session would target perhaps the same audience, but looking at the program, I see it’s no longer the case. Thanks the power that be!

I also organise a session on “Bayesian computation in Neurosciences” in MCMski 4. Feel free to come if you have interest in the subject. Myself, I think it’s a particular cool area of application, about which I know very very little… which is why I organise a session to learn more about it! :-) I also co-organise (with Simon Barthelmé and Adam Johansen) a workshop at Warwick on the same subject, more details soon.

## another nice one from Elsevier

Posted in General by nicolaschopin on 23 December 2013

In case you have missed the new round of misdeeds by Elsevier, here is an excellent summary (plus a good overview of the current debate on open access an so on):