Introduction to “Scalable inference for Markov processes with intractable likelihoods” by J. Owen, D. Wilkinson, C. Gillespie
Ryder, Robin J. (2015), Introduction to “Scalable inference for Markov processes with intractable likelihoods” by J. Owen, D. Wilkinson, C. Gillespie, Statistics and Computing, 25, 1, p. 143. http://dx.doi.org/10.1007/s11222-014-9534-5
TypeArticle accepté pour publication ou publié
Nom de la revueStatistics and Computing
MétadonnéesAfficher la notice complète
Auteur(s)Ryder, Robin J.
Résumé (EN)Approximate Bayesian computation (ABC) was one of the major themes of MCMSki 2014, with several talks and especially many posters devoted to advances and applications of this method. The approach of Owen, Wilkinson and Gillespie is original, since it uses ABC not to perform direct (approximate) sampling from the posterior, but as an exploratory tool to be used to find a good initialization point for another technique. For the area of application considered (dynamical systems), there as long been a debate about the most useful method between ABC, which gives an approximate answer in a not-unreasonable time, and particle Markov chain Monte Carlo (pMCMC), which is asymptotically exact but easily runs into convergence issues. By using the output of ABC as an initialization for pMCMC, we can get the best of both worlds. This of course generalizes to other MCMC algorithms. The authors have presented convincing results on the Lotka-Volterra model and other dynamical systems.
Mots-clésparticle Markov chain Monte Carlo; dynamical systems; Approximate Bayesian computation
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