Archive for computational statistics

I am cold all over…

Posted in Books, Kids, Statistics, University life with tags , , , , , , , on October 29, 2014 by xi'an

unusual snowfall on Bois de Boulogne, March 12, 2013An email from one of my Master students who sent his problem sheet (taken from Monte Carlo Statistical Methods) late:

Bonsoir Professeur
Je « suis » votre cours du mercredi dont le formalisme mathématique me fait froid partout
Avec beaucoup de difficulté je vous envoie mes exercices du premier chapitre de votre livre.

which translates as

Good evening Professor,
I “follow” your Wednesday class which mathematical formalism makes me cold all over. With much hardship, I send you the first batch of problems from your book.

I know that winter is coming, but, still, making students shudder from mathematical cold is not my primary goal when teaching Monte Carlo methods!

future of computational statistics

Posted in Books, pictures, R, Statistics, University life with tags , , , , , , , , , , , , , , on September 29, 2014 by xi'an

I am currently preparing a survey paper on the present state of computational statistics, reflecting on the massive evolution of the field since my early Monte Carlo simulations on an Apple //e, which would take a few days to return a curve of approximate expected squared error losses… It seems to me that MCMC is attracting more attention nowadays than in the past decade, both because of methodological advances linked with better theoretical tools, as for instance in the handling of stochastic processes, and because of new forays in accelerated computing via parallel and cloud computing, The breadth and quality of talks at MCMski IV is testimony to this. A second trend that is not unrelated to the first one is the development of new and the rehabilitation of older techniques to handle complex models by approximations, witness ABC, Expectation-Propagation, variational Bayes, &tc. With a corollary being an healthy questioning of the models themselves. As illustrated for instance in Chris Holmes’ talk last week. While those simplifications are inevitable when faced with hardly imaginable levels of complexity, I still remain confident about the “inevitability” of turning statistics into an “optimize+penalize” tunnel vision…  A third characteristic is the emergence of new languages and meta-languages intended to handle complexity both of problems and of solutions towards a wider audience of users. STAN obviously comes to mind. And JAGS. But it may be that another scale of language is now required…

If you have any suggestion of novel directions in computational statistics or instead of dead ends, I would be most interested in hearing them! So please do comment or send emails to my gmail address bayesianstatistics

Bayesian computational tools

Posted in R, Statistics, University life with tags , , , , on June 18, 2013 by xi'an

fig4I just updated my short review on Bayesian computational tools I first wrote in April for the Annual Review of Statistics and Its Applications. The coverage is quite restricted, as I took advantage of two phantom papers I had started a while ago, one with Jean-Michel Marin, on hierarchical Bayes methods and on ABC. (As stressed in the first version, the paper handles missing data, not as a topic, but as a fact!) The running example is the Laplace vs. Gauss model choice problem, first considered in our ABC model choice paper. The referee of the paper was asking for a broader perspective, which makes perfect sense (except that I did not have the time to get that broad). And mentioned a potential missing acknowledgement of priority as Olli’s thesis was using a simple (instead of double) exponential vs. Gauss as its running example. Once again, a plain 25 pages introduction to the topic, not aiming at anything new. The exercise made me ponder whether or not I wanted to engage into it in a near future, with a pessimistic outcome!

ICERM, Brown, Providence, RI (#3)

Posted in pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , on December 3, 2012 by xi'an

ICERM building, Providence, RI, Nov. 29, 2012Just yet another perfect day in Providence! Especially when I thought it was going to be a half-day: After a longer and slightly warmer run in the early morning around the peninsula, I attended the lecture by Eric Moulines on his recent results on adaptive MCMC and the equi-energy sampler. At this point, we were told that, since Peter Glynn was sick, the afternoon talks were drifted forward. This meant that I could attend Mylène Bédard’s talk in the morning and most of Xiao-Li Meng’s talk, before catching my bus to the airport, making it a full day in the end!

The research presented by Mylène (and coauthored with Randal Douc and Eric Moulines) was on multiple-try MCMC and delayed-rejection MCMC, with optimal scaling results and a comparison of the efficiency of those involved schemes. I had not seen the work before and got quite impressed by the precision of the results and the potential for huge efficiency gains. One of the most interesting tricks was to use an antithetic move for the second step, considerably improving the acceptance rate in the process. An aside exciting point was to realise that the hit-and-run solution was also open to wide time-savings thanks to some factorisation.

DSC_3532While Xiao-Li’s talk had connections with his earlier illuminating talk in New York last year, I am quite desolate to have missed [the most novel] half of it (and still caught my bus by a two minute margin!), esp. because it connected beautifully with the constant estimation controverse! Indeed, Xiao-Li started his presentation with the pseudo-paradox that the likelihood cannot be written as a function of the normalising constant, simply because this is not a free parameter. He then switched to his usual theme that the dominating measure was to be replaced with a substitute and estimated.The normalising constant being a function of the dominating measure, it is a by-product of this estimation step. And can even be endowed within a Bayesian framework. Obviously, one can always argue against the fact that the dominating measure is truly unknown, however this gives a very elegant safe-conduct to escape the debate about the constant that did not want to be estimated…So to answer Xiao-Li’s question as I was leaving the conference room, I have now come to a more complete agreement with his approach. And think further advances could be contemplated along this path…

ICERM, Brown, Providence, RI (#2)

Posted in pictures, Running, Statistics, Travel, University life with tags , , , , , , , on November 30, 2012 by xi'an

ICERM building by the canal, Providence, RI, Nov. 29, 2012Just another perfect day in Providence! After a brisk run in the eearly morning which took me through Brown campus, I attended the lecture by Sean Meyn on feedback particle filters. As it was mostly on diffusions with control terms, just too far from my field, I missed most of the points. (My fault, not Sean’s!) Then Ramon von Handel gave a talk about the curse(s) of dimensionality in particle filters, much closer to my interests, with a good summary of why (optimal) filters were not suffering from a curse in n, the horizon size, but in d, the dimension of the space, followed by an argument that some degree of correlation decay could overcome this dimensional curse as well. After the lunch break (where I thought further about the likelihood principle!), Dana Randall gave a technical talk on mixing properties of the hardcore model on Z² and bounding the cutoff parameter, which is when I appreciated the ability to follow talks from the ICERM lounge, watching slides and video of the talk taking place on the other side of the wall! At last, and in a programming contrapoint from slowly mixing to fastest mixing, Jim Fill presented his recent work on ordering Markov chains and finding fastest-mixing chains, which of course reminded me of Peskun ordering although there may be little connection in the end. The poster session in the evening had sufficiently few posters to make the discussion with each author enjoyable and relevant.A consistent feature of the meeting thus, allowing for quality interacting time between participants. I am now looking forward the final day with a most intriguing title by my friend Eric Moulines on TBA…

ICERM, Brown, Providence, RI (#0)

Posted in Running, Statistics, Travel, University life with tags , , , , , , , , on November 28, 2012 by xi'an

I have just arrived in Providence, RI, for the ICERM workshop on Performance Analysis of Monte Carlo Methods. While the plane trip was uneventful and even relaxing, as I could work on the revision to our ABCel (soon to be BCel!) paper, the bus trip from Boston to Providence, while smooth, quiet, wirelessed, and on-time, was a wee too much as it was already late for my standards… Anyway, I am giving one of the talks tomorrow, with a pot-pourri on ABC and empirical likelihood as in Ames and Chicago last month. The format of the workshop sounds very nice, with only four talks a day, which should leave a lot of space for interactions between participants (if I do not crash from my early early rise…) And, as mentioned earlier, I am looking forward visiting the futuristic building.

deaths on Mont Maudit (and bad graph)

Posted in Mountains, Statistics, Travel with tags , , , , , , , , , , on July 14, 2012 by xi'an

A large avalanche on one of the most classical routes to the summit of Mont Blanc early yesterday morning alas caught several groups climbing towards Mont Blanc and most sadly killed nine of them, including the BMC former secretary Roger Payne. It was triggered by a sérac fall above the route, always a possibility in this area whose name says it all… At a very minor level, compared with those tragic deaths, let me point out that The Guardian reported on this tragic accident with the following “bad graph”, which apparently gives the number of deaths on each accident (month?) with no consideration for the time (first axis). In other words, the barplot is missing all the zeroes. (It also missed the group of two who died from exhaustion in the Jorasses last November…)

This is not the right post to elaborate on the announcement that the next MCM’ski conference will take place in Chamonix, presumably on January 6-8, 2014. I am currently waiting for a formal proposal from the conference bureau in Chamonix… Nor on the incoming creation of a computational Bayes (BayesComp) ISBA section [if enough ISBA members support this creation].

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