Archive for Montpellier

trip to Montpellier

Posted in pictures, Statistics, Travel, University life, Wines with tags , , , , , , on February 27, 2014 by xi'an

IMG_2477Last week, I flew down to Montpellier for two days of work on ABC model choice with Jean-Michel Marin and Pierre Pudlo. Although we missed the COLT 2014 deadline, we are now close to completing this work that will propose a rather radical change in our advocacy of how ABC model choice should be conducted. We actually spent the second day on the wonderful campus of INRA at Montferrier-sur-Lez, just outside Montpellier, discussing of the implications of this approach with our friends at CBGP, Jean-Marie Cornuet and Arnaud Estoup. With possible impact on the DIYABC software. It was a very profitable trip (not mentioning tasting great Grés de Montpellier wine!) and I hope to manage completing the paper with Pierre during the next week in Banff. Unfortunately, when I came back to my train station, I found some idiots had a go at my bike and bent the back wheel which then needed to be replaced…

art brut

Posted in pictures, Travel with tags , , , , on February 8, 2014 by xi'an

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Saint Christol white & red

Posted in Travel, Wines with tags , , , , , , on September 20, 2013 by xi'an

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ABC for design

Posted in Statistics with tags , , , , , , , on August 30, 2013 by xi'an

I wrote a comment on this arXived paper on simulation based design that starts from Müller (1999) and gets an ABC perspective a while ago on my iPad when travelling to Montpellier and then forgot to download it…

Hainy, [Wener] Müller, and Wagner recently arXived a paper called “Likelihood-free Simulation-based Optimal Design“, paper which relies on ABC to construct optimal designs . Remember that [Peter] Müller (1999) uses a natural simulated annealing that is quite similar to our MAP [SAME] algorithm with Arnaud Doucet and Simon Godsill, relying on multiple versions of the data set to get to the maximum. The paper also builds upon our 2006 JASA paper with my then PhD student Billy Amzal, Eric Parent, and Frederic Bois, paper that took advantage of the then emerging particle methods to improve upon a static horizon target. While our method is sequential in that it pursues a moving target, it does not rely on the generic methodology developed by del Moral et al. (2006), where a backward kernel brings more stability to the moves. The paper also implements a version of our population Monte Carlo ABC algorithm (Beaumont et al., 2009), as a first step before an MCMC simulation. Overall, the paper sounds more like a review than like a strongly directive entry into ABC based design in that it remains quite generic. Not that I have specific suggestions, mind!, but I fear a realistic implementation (as opposed to the linear model used in the paper) would require a certain amount of calibration. There are missing references of recent papers using ABC for design, including some by Michael Stumpf I think.

I did not know about the Kuck et al. reference… Which is reproducing our 2006 approach within the del Moral framework. It uses a continuous temperature scale that I find artificial and not that useful, again a maybe superficial comment as I didn’t get very much into the paper … Just that integer powers lead to multiples of the sample and have a nice algorithmic counterpart.

relevant statistics for Bayesian model choice (#4)

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , on August 23, 2013 by xi'an

leaves from a pergola reflecting on a truck, Crès, near Montpellier, June 14, 2012I have just posted on arXiv the fourth (and hopefully final) version of our paper, Relevant statistics for Bayesian model choice, written jointly with Jean-Michel Marin, Natesh Pillai, and Judith Rousseau over the past two years. As we received a very positive return from the editorial team at JRSS Series B, I flew to Montpellier today to write & resubmit a revised version of the paper. The changes are only stylistic, since we could not answer in depth a query about the apparently different speeds of convergence of the posterior probabilities under the Gaussian and Laplace distributions in Figures 3 & 4 (see paper). This was a most interesting question in that the marginal likelihoods do indeed seem to converge at different speeds. However, the only precise information we can derive from our result (Theorem 1) is when the Bayes factor is not consistent. Otherwise, we only have a lower bound on its speed of convergence (under the correct model). Getting precise speeds in this case sounds beyond our reach… (Unless I am confused with time zones, this post should come alive just after the fourth version is announced on arXiv..)

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