advanced computational methods for complex models in Biology [talk]

Here are the slides of the presentation I gave at the EPSRC Advanced Computational methods for complex models in Biology at University College London, last week. Introducing random forests as proper summaries for both model choice and parameter estimation (with considerable overlap with earlier slides, obviously!). The other talks of that highly interesting day on computational Biology were mostly about ancestral graphs, using Wright-Fisher diffusions for coalescents, plus a comparison of expectation-propagation and ABC on a genealogy model by Mark Beaumont and the decision theoretic approach to HMM order estimation by Chris Holmes. In addition, it gave me the opportunity to come back to the Department of Statistics at UCL more than twenty years after my previous visit, at a time when my friend Costas Goutis was still there. And to realise it had moved from its historical premises years ago. (I wonder what happened to the two staircases built to reduce frictions between Fisher and Pearson if I remember correctly…)

Monte Carlo in the convent

Last week, at the same time as the workshop on retrospective Monte Carlo in Warwick, there was a Monte Carlo conference in Paris, closing a Monte Carlo cycle run by Institut Louis Bachelier from October 2015 till June 2016. It took place in the convent of Les Cordeliers, downtown Paris [hence the title] and I alas could not attend the talks. As I organised a session on Bayesian (approximate) computations, with Richard Everitt, Jere Koskela, and Chris Sherlock as speakers (and Robin Ryder as chair), here are the slides of the speakers (actually, Jere most kindly agreed to give Chris’ talk as Chris was to sick to travel to Paris):

ABC à l’X [back]

“It is difficult to ensure that such sophisticated battles against the ‘ε-dilemma’ that arise in the simulation based inferential approaches of ABC and ALC do not confound the true posterior.” Shainudin et al., 2010

It was a very interesting talk that took place at Polytechnique last afternoon. 9Although one could argue that the title was misleading in that ABC was never truly used, except as a scarecrow!) Indeed, Razeesh Shainudin gave a lively one-hour lecture where he showed that, for the standard Wright-Fisher coalescent model, the exact likelihood of the site-frequency-spectrum statistic (SFS). Ìt would have required much more time to cover the dense material contained in the paper he coauthored on this topic (and to appear in the Bulletin of Mathematical Biology, Algebraic Biology Special Edition), but his message was quite clear, namely that a graph analysis of the distribution of the SFS statistic was permitting a closed-form representation of the likelihood. And similarly for some linear combinations of the SFS that allowed for [great!] Kemeny and Snell‘s (1968) lumpability criterion to apply, namely for an impoverished Markov chain defined on an aggregated state space to remain Markov. I do see the direct consequence on the quality of ABC (except when conducting Monte Carlo experiments to compare both outputs) and I have not thought long enough to spot the impact on our current research, but appreciated very much the intuition given in the talk. The idea beyond the method seems to be that improving the support of the importance sampling distribution in order to remove parameter values that could not lead to the real data (SFS) provides a clear efficiency improvement)