day five at ISBA 22

Woke up even earlier today! Which left me time to work on switching to Leonard Cohen’s song titles for my slide frametitles this afternoon (last talk of the whole conference!), run once again to Mon(t) Royal as all pools are closed (Happy Canada Day!, except to “freedom convoy” antivaxxxers.) Which led to me meeting a raccoon by the side of the path (and moroons feeding wildlife).

Had an exciting time at the morning session, where Giacomo Zanella (formerly Warwick) talked on a mixture approach to leave-one-out predictives, with pseudo-harmonic mean representation, averaging inverse density across all observations. Better than harmonic? Some assumptions allow for finite variance, although I am missing the deep argument (in part due to Giacomo’s machine-gun delivery pace!) Then Alicia Corbella (Warwick) presented a promising entry into PDMP by proposing an automated zig-zag sampler. Pointing out on the side to Joris Bierkens’ webpage on the state-of-the-art PDMP methodology. In this approach, joint with with my other Warwick colleagues Simon Spencer and Gareth Roberts, the zig-zag sampler relies on automatic differentiation and sub-sampling and bound derivation, with “no further information on the target needed”. And finaly Chris Carmona presented a joint work with Geoff Nicholls that is merging merging cut posteriors and variational inference to create a meta posterior. Work and talk were motivated by a nice medieval linguistic problem where the latent variables impact the (convergence of the) MCMC algorithm [as in our k-nearest neighbour experience]. Interestingly using normalising [neural spline] flows. The pseudo-posterior seems to depend very much on their modularization rate η, which penalises how much one module influences the next one.

In the aft, I attended sort of by chance [due to a missing speaker in the copula session] to the end of a session on migration modelling, with a talk by Jason Hilton and Martin Hinsch focussing on the 2015’s mass exodus of Syrians through the Mediterranean,  away from the joint evils of al-Hassad and ISIS. As this was a tragedy whose modelling I had vainly tried to contribute to, I was obviously captivated and frustrated (leaning of the IOM missing migrant project!) Fitting the agent-based model was actually using ABC, and most particularly our ABC-PMC!!!

My own and final session had Gareth (Warwick) presenting his recent work with Jun Yang and Kryzs Łatuszyński (Warwick) on the stereoscopic projection improvement over regular MCMC, which involves turning the target into a distribution supported by an hypersphere and hence considering a distribution with compact support and higher efficiency. Kryzs had explained the principle while driving back from Gregynog two months ago. The idea is somewhat similar to our origaMCMC, which I presented at MCqMC 2016 in Stanford (and never completed), except our projection was inside a ball. Looking forward the adaptive version, in the making!

And to conclude this subjective journal from the ISBA conference, borrowing this title by (Westmount born) Leonard Cohen, “Hey, that’s not a way to say goodbye”… To paraphrase Bilbo Baggins, I have not interacted with at least half the participants half as much as I would have liked. But this was still a reunion, albeit in the new Normal. Hopefully, the conference will not have induced a massive COVID cluster on top of numerous scientific and social exchanges! The following days will tell. Congrats to the ISBA 2022 organisers for achieving a most successful event in these times of uncertainty. And looking forward the 2024 next edition in Ca’Foscari, Venezia!!!

 

day one at ISBA 22

Started the day with a much appreciated swimming practice in the [alas warm⁺⁺⁺] outdoor 50m pool on the Island with no one but me in the slooow lane. And had my first ride with the biXi system, surprised at having to queue behind other bikes at red lights! More significantly, it was a great feeling to reunite at last with so many friends I had not met for more than two years!!!

My friend Adrian Raftery gave the very first plenary lecture on his work on the Bayesian approach to long-term population projections, which was recently  a work censored by some US States, then counter-censored by the Supreme Court [too busy to kill Roe v. Wade!]. Great to see the use of Bayesian methods validated by the UN Population Division [with at least one branch of the UN

Stephen Lauritzen returning to de Finetti notion of a model as something not real or true at all, back to exchangeability. Making me wonder when exchangeability is more than a convenient assumption leading to the Hewitt-Savage theorem. And sufficiency. I mean, without falling into a Keynesian fallacy, each point of the sample has unique specificities that cannot be taken into account in an exchangeable model. Nice to hear some measure theory, though!!! Plus a comment on the median never being sufficient, recouping an older (and presumably not original) point of mine. Stephen’s (or Fisher’s?) argument being that the median cannot be recursively computed!

Antonietta Mira and I had our ABC session this afternoon with Cecilia Viscardi, Sirio Legramanti, and Massimiliano Tamborino (Warwick) as speakers. Cecilia linked ABC with normalising flows, in collaboration with Dennis Prangle (whose earlier paper on this connection was presented as the first One World ABC seminar). Thus using past simulations to approximate the posterior by a neural network, possibly with a significant increase in computing time when compared with more rudimentary SMC-ABC methods in larger dimensions. Sirio considered summary-free ABC based on discrepancies like Rademacher complexity. Which more or less contains MMD, Kullback-Leibler, Wasserstein and more, although it seems to be dependent on the parameterisation of the observations. An interesting opening at the end was that this approach could apply to non iid settings. Massi presented a paper coauthored with Umberto that had just been arXived. On sequential ABC with a dependence on the summary statistic (hence guided). Further bringing copulas into the game, although this forces another choice [for the marginals] in the method.

Tamara Broderick talked about a puzzling leverage effect of some observations in economic studies where a tiny portion of individuals may modify the significance or the sign of a coefficient, for which I cannot tell whether the data or the reliance on statistical significance are to blame. Robert Kohn presented mixture-of-Gaussian copulas [not to be confused with mixture of Gaussian-copulas!] and Nancy Reid concluded my first [and somewhat exhausting!] day at ISBA with a BFF talk on the different statistical paradigms take on confidence (for which the notion of calibration seems to remain frequentist).

Side comments: First, most people in the conference are wearing masks, which is great! Also, I find it hard to read slides from the screen, which I presume is an age issue (?!) Even more aside, I had Korean lunch in a place that refused to serve me a glass of water, which I find amazing.

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.

 

Christian Robert is giving a talk in Jussieu tomorrow

My namesake Christian (Yann) Robert (CREST) is giving a seminar tomorrow in Jussieu (Université Pierre & Marie Curie, couloir 16-26, salle 209), between 2 and 3, on composite likelihood estimation method for hierarchical Archimedean copulas defined with multivariate compound distributions. Here is the abstract:

We consider the family of hierarchical Archimedean copulas obtained from multivariate exponential mixture distributions through compounding, as introduced by Cossette et al. (2017). We investigate ways of determining the structure of these copulas and estimating their parameters. An agglomerative clustering technique based on the matrix of Spearman’s rhos, combined with a bootstrap procedure, is used to identify the tree structure. Parameters are estimated through a top-down composite likelihood. The validity of the approach is illustrated through two simulation studies in which the procedure is explained step by step. The composite likelihood method is also compared to the full likelihood method in a simple case where the latter is computable.

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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.