The 5th Workshop on Sequential Monte Carlo Methods (SMC 2022) will take place in Madrid on 4-6 May 2022. More precisely on the Leganés campus of Universidad Carlos III de Madrid. Registrations are now open, with very modest registration fees and the list of invited speakers is available on the webpage of the workshop. (The SMC 2020 workshop was cancelled due to the COVID-19 pandemic. An earlier workshop took place at CREST in 2015.)
Archive for SMC 2015
SMC 22 coming soon!
Posted in Statistics with tags computational inference, CREST, Madrid, Malakoff, particle filters, sequential Monte Carlo, SMC 2015, SMC 2022, Spain, Universidad Carlos III de Madrid on February 7, 2022 by xi'anSMC 2015
Posted in Statistics, Travel, University life with tags Bayesian computation, coupling, CREST, ENSAE, gender imbalance, MCMC, Monte Carlo Statistical Methods, multiscale, sequential Monte Carlo, SMC 2015, unbiasedness on September 7, 2015 by xi'an
Nicolas Chopin ran a workshop at ENSAE on sequential Monte Carlo the past three days and it was a good opportunity to get a much needed up-to-date on the current trends in the field. Especially given that the meeting was literally downstairs from my office at CREST. And given the top range of researchers presenting their current or past work (in the very amphitheatre where I attended my first statistics lectures, a few dozen years ago!). Since unforeseen events made me miss most of the central day, I will not comment on individual talks, some of which I had already heard in the recent past, but this was a high quality workshop, topped by a superb organisation. (I started wondering why there was no a single female speaker in the program and so few female participants in the audience, then realised this is a field with a massive gender imbalance, which is difficult to explain given the different situation in Bayesian statistics and even in Bayesian computation…) Some key topics I gathered during the talks I could attend–apologies to the other speakers for missing their talk due to those unforeseen events–are unbiasedness, which sounds central to the SMC methods [at least those presented there] as opposed to MCMC algorithms, and local features, used in different ways like hierarchical decomposition, multiscale, parallelisation, local coupling, &tc., to improve convergence and efficiency…
