ABC in Svalbard [update]

Even though no one can tell at this stage who will be allowed to travel to Svalbard mid April 2021, we are keeping the workshop to physically take place as planned in Longyearbyen. With at least a group of volunteers made of researchers from Oslo (since at the current time, travel between mainland Norway and Svalbard is authorised). The conference room reservation has been confirmed yesterday and there are a few hotel rooms pre-booked through Hurtigrutensvlabard.com. Anyone planning to attend just need to (i) register on the workshop webpage, (ii) book an hotel room for the duration of the workshop (or more)., and (iii) reserve a plane ticket as there are not that many flights planned.

Obviously this option should only attract a few brave souls (from nearby countries). We are thus running at the same time three mirror workshops in Brisbane (QUT), Coventry (University of Warwick), and Grenoble (IMAG & INRIA). Except for Warwick, where the current pandemic restrictions do not allow for a workshop to take place, the mirror workshops will take place in university buildings and be face-to-face (with video connections as well). Julyan Arbel has set-up a mirror webpage as well. With a (free) registration deadline of 31 March, the workshop being open to all who can attend. Hopefully enough of us will gather here or there to keep up with the spirit of the earlier ABC workshops. (To make the mirror places truly ABCesque, it should have been set in A as Autrans rather than Grenoble!)

computing Bayes 2.0

Our survey paper on “computing Bayes“, written with my friends Gael Martin [who led this project most efficiently!] and David Frazier, has now been revised and resubmitted, the new version being now available on arXiv. Recognising that the entire range of the literature cannot be encompassed within a single review, esp. wrt the theoretical advances made on MCMC, the revised version is more focussed on the approximative solutions (when considering MCMC as “exact”!). As put by one of the referees [which were all very supportive of the paper], “the authors are very brave. To cover in a review paper the computational methods for Bayesian inference is indeed a monumental task and in a way an hopeless one”. This is the opportunity to congratulate Gael on her election to the Academy of Social Sciences of Australia last month. (Along with her colleague from Monash, Rob Hyndman.)

Data Science & Machine Learning book free for download

David Frazier’s talk on One World ABC seminar tomorrow [watch for the time!]

My friend and coauthor from Melbourne is giving the One World ABC seminar tomorrow. He will be talking at 10:30 UK time, 11:30 Brussels time, and 20:30 Melbourne time! On Robust and Efficient Approximate Bayesian Computation: A Minimum Distance Approach. Be on time!

Approximate Bayesian analysis of (un)conditional copulas [webinar]

The Algorithms & Computationally Intensive Inference seminar (access by request) will virtually resume this week in Warwick U on Friday, 18 Sept., at noon (UK time, ie +1GMT) with a talk by (my coauthor and former PhD student) Clara Grazian (now at UNSW), talking about approximate Bayes for copulas:

Many proposals are now available to model complex data, in particular thanks to the recent advances in computational methodologies and algorithms which allow to work with complicated likelihood function in a reasonable amount of time. However, it is, in general, difficult to analyse data characterized by complicated forms of dependence. Copula models have been introduced as probabilistic tools to describe a multivariate random vector via the marginal distributions and a copula function which captures the dependence structure among the vector components, thanks to the Sklar’s theorem, which states that any d-dimensional absolutely continuous density can be uniquely represented as the product of the marginal distributions and the copula function. Major areas of application include econometrics, hydrological engineering, biomedical science, signal processing and finance. Bayesian methods to analyse copula models tend to be computational intensive or to rely on the choice of a particular copula function, in particular because methods of model selection are not yet fully developed in this setting. We will present a general method to estimate some specific quantities of interest of a generic copula by adopting an approximate Bayesian approach based on an approximation of the likelihood function. Our approach is general, in the sense that it could be adapted both to parametric and nonparametric modelling of the marginal distributions and can be generalised in presence of covariates. It also allow to avoid the definition of the copula function. The class of algorithms proposed allows the researcher to model the joint distribution of a random vector in two separate steps: first the marginal distributions and, then, a copula function which captures the dependence structure among the vector components.