Archive for Montpellier
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.
I 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..)