Archive for Journal of the Royal Statistical Society
right place, wrong version
Posted in Statistics with tags ABC, Approximate Bayesian computation, copyediting, HAL, Helvetica, Journal of the Royal Statistical Society, Royal Statistical Society, Series B, typos, Wasserstein distance on August 12, 2020 by xi'anmisspecified [but published!]
Posted in Statistics with tags ABC, Approximate Bayesian computation, Journal of the Royal Statistical Society, JRSSB, misspecified model, Series B on April 1, 2020 by xi'anunbiased MCMC discussed at the RSS tomorrow night
Posted in Books, Kids, pictures, Statistics, Travel, University life with tags AABI, coupling, discussion paper, Journal of the Royal Statistical Society, Markov chain Monte Carlo algorithm, MCMC, Read paper, Royal Statistical Society, Series B, unbiasedness, Université Paris Dauphine, Vancouver on December 10, 2019 by xi'anThe paper ‘Unbiased Markov chain Monte Carlo methods with couplings’ by Pierre Jacob et al. will be discussed (or Read) tomorrow at the Royal Statistical Society, 12 Errol Street, London, tomorrow night, Wed 11 December, at 5pm London time. With a pre-discussion session at 3pm, involving Chris Sherlock and Pierre Jacob, and chaired by Ioanna Manolopoulou. While I will alas miss this opportunity, due to my trip to Vancouver over the weekend, it is great that that the young tradition of pre-discussion sessions has been rekindled as it helps put the paper into perspective for a wider audience and thus makes the more formal Read Paper session more profitable. As we discussed the paper in Paris Dauphine with our graduate students a few weeks ago, we will for certain send one or several written discussions to Series B!
a good start in Series B!
Posted in Books, pictures, Statistics, University life with tags ABC, approximate Bayesian inference, generative model, Journal of the Royal Statistical Society, Olympic National Park, peer review, Series B, sunrise, Wasserstein distance on January 5, 2019 by xi'anJust received the great news for the turn of the year that our paper on ABC using Wasserstein distance was accepted in Series B! Inference in generative models using the Wasserstein distance, written by Espen Bernton, Pierre Jacob, Mathieu Gerber, and myself, bypasses the (nasty) selection of summary statistics in ABC by considering the Wasserstein distance between observed and simulated samples. It focuses in particular on non-iid cases like time series in what I find fairly innovative ways. I am thus very glad the paper is going to appear in JRSS B, as it has methodological consequences that should appeal to the community at large.