HW AMS & EPSRC MAG-MIGS CDT seminar

Some explanation for all these acronyms! I am giving a Actuarial Mathematics & Statistics (AMS) seminar at Heriot-Watt (HW) University, in Edinburgh, tomorow. But in the (new) Bayes Centre, at the University of Edinburgh, rather than on the campus of Heriot-Watt, as this is also the launching day of the Centre for Doctoral Training (CDT) on Mathematical Modelling, Analysis, & Computation (MAG) shared between Heriot-Watt, and the University of Edinburgh, funded by the EPSRC and located in the Maxwell Institute Graduate School (MIGS) in its Bayes Centre. My talk will be on ABC convergence and misspecification.

asymptotic properties of ABC now appeared

Our paper with David Frazier, Gael Martin and Judith Rousseau has appeared in print in Biometrika, Volume 105, Issue 3, 1 September 2018, Pages 593–607, almost exactly two years after it was submitted. I am quite glad with the final version, though, and grateful for the editorial input, as the paper clearly characterises the connection between the tolerance level ε and the convergence rate of the summary statistic to its parameter identifying asymptotic mean. Asymptotic in the sample size, that is.

ABC’ptotics on-line

Our paper on Asymptotic properties of ABC with David Frazier, Gael Martin, and Judith Rousseau, is now on-line on the Biometrika webpage. Coincidentally both papers by Wentao Li and Paul Fearnhead on ABC’ptotics are published in the June issue of the journal.

Approximate Bayesian computation allows for statistical analysis using models with intractable likelihoods. In this paper we consider the asymptotic behaviour of the posterior distribution obtained by this method. We give general results on the rate at which the posterior distribution concentrates on sets containing the true parameter, the limiting shape of the posterior distribution, and the asymptotic distribution of the posterior mean. These results hold under given rates for the tolerance used within the method, mild regularity conditions on the summary statistics, and a condition linked to identification of the true parameters. Implications for practitioners are discussed.

back to Wales [54th Gregynog Statistical Conference]

Today, provided the Air France strike let me fly to Birmingham airport!, I am back at Gregynog Hall, Wales, for the weekend conference organised there every year by some Welsh and English statistics departments, including Warwick. Looking forward to the relaxed gathering in the glorious Welsh countryside (and hoping that my knee will have sufficiently recovered for some trail running around Gregynog Hall…!) Here are the slides of the talk I will present tomorrow:

impressions from EcoSta2017 [guest post]

[This is a guest post on the recent EcoSta2017 (Econometrics and Statistics) conference in Hong Kong, contributed by Chris Drovandi from QUT, Brisbane.]

There were (at least) two sessions on Bayesian Computation at the recent EcoSta (Econometrics and Statistics) 2017 conference in Hong Kong. Below is my review of them. My overall impression of the conference is that there were lots of interesting talks, albeit a lot in financial time series, not my area. Even so I managed to pick up a few ideas/concepts that could be useful in my research. One criticism I had was that there were too many sessions in parallel, which made choosing quite difficult and some sessions very poorly attended. Another criticism of many participants I spoke to was that the location of the conference was relatively far from the city area.

In the first session (chaired by Robert Kohn), Minh-Ngoc Tran spoke about this paper on Bayesian estimation of high-dimensional Copula models with mixed discrete/continuous margins. Copula models with all continuous margins are relatively easy to deal with, but when the margins are discrete or mixed there are issues with computing the likelihood. The main idea of the paper is to re-write the intractable likelihood as an integral over a hypercube of ≤J dimensions (where J is the number of variables), which can then be estimated unbiasedly (with variance reduction by using randomised quasi-MC numbers). The paper develops advanced (correlated) pseudo-marginal and variational Bayes methods for inference.

In the following talk, Chris Carter spoke about different types of pseudo-marginal methods, particle marginal Metropolis-Hastings and particle Gibbs for state space models. Chris suggests that a combination of these methods into a single algorithm can further improve mixing.