Archive for Santa Margherita di Pula

Chateau Mussar

Posted in pictures, Statistics, Travel, Wines with tags , , , , , on July 20, 2016 by xi'an

ISBA 2016 [#7]

Posted in Mountains, pictures, Running, Statistics, Travel, Wines with tags , , , , , , , , , , , , , on June 20, 2016 by xi'an

This series of posts is most probably getting by now an imposition on the ‘Og readership, which either attended ISBA 2016 and does (do?) not need my impressions or did not attend and hence does (do?) not need vague impressions about talks they (it?) did not see, but indulge me in reminiscing about this last ISBA meeting (or more reasonably ignore this post altogether). Now that I am back home (with most of my Sard wine bottles intact!, and a good array of Sard cheeses).

This meeting seems to be the largest ISBA meeting ever, with hundreds of young statisticians taking part in it (despite my early misgivings about the deterrent represented by the overall cost of attending the meeting. I presume holding the meeting in Europe made it easier and cheaper for most Europeans to attend (and hopefully the same will happen in Edinburgh in 2018!), as was the (somewhat unsuspected) wide availability of rental alternatives in the close vicinity of the conference resort. I also presume the same travel opportunities would not have been true in Banff, although local costs would have been lower. It was fantastic to see so many new researchers interested in Bayesian statistics and to meet some of them. And to have more sessions run by the j-Bayes section of ISBA (although I found it counterproductive that such sessions do not focus on a thematically coherent theme). As a result, the meeting was more intense than ever and I found it truly exhausting, despite skipping most poster sessions. Maybe also because I did not skip a single session thanks to the availability of an interesting theme for each block in the schedule. (And because I attended more [great] Sard dinners than I originally intended.) Having five sessions in parallel indeed means there is a fabulous offer of themes for every taste. It also means there are inevitably conflicts when picking one’s session.

Back to poster sessions, I feel I missed an essential part of the meeting, which made ISBA meetings so unique, but it also seems to me the organisation of those sessions should be reconsidered against the rise in attendance. (And my growing inability to stay up late!) One solution suggested by my recent AISTATS experience is to select posters towards lowering the number of posters in the four poster sessions. The success rate for the Cadiz meeting was 35%.) The obvious downsizes are the selection process (but this was done quite efficiently for AISTATS) and the potential reduction in the number of participants. A medium ground could see a smaller fraction of posters to be selected by this process (and published one way or another as in machine-learning conferences) and presented during the evening poster sessions, with other posters being given during the coffee breaks [which certainly does not help in reducing the intensity of the schedule]. Another and altogether solution is to extend the parallelism of oral sessions to poster sessions, by regrouping them into five or six themes or keywords chosen by the presenters and having those presented in different rooms to split the attendance down to human level and tolerable decibels. Nothing preventing participants to visit several rooms in a given evening. Or to keep posters for several nights in a row if the number of rooms allows.

It may also be that this edition of ISBA 2016 sees the end of the resort-style meeting in the spirit of the early Valencia meetings. Edinburgh 2018 will certainly be an open-space conference in that meals and lodgings will be “on” the participants who may choose where and how much. I have heard many times the argument that conferences held in single hotels or resorts facilitated the contacts between young and senior researchers, but I fear this is not sustainable against the growth of the audience. Holding the meeting in a reasonably close and compact location, as a University building, should allow for a sufficient degree of interaction, as was the case at ISBA 2016. (Kerrie Mengersen also suggested that a few restaurants nearby could be designated as “favourites” for participants to interact at dinner time.) Another suggestion to reinforce networking and interacting would be to hold more satellite workshops before the main conference. It seems there could be a young Bayesian workshop in England the prior week as well as a summer short course on simulation methods.

Organising meetings is getting increasingly complex and provides few rewards at the academic level, so I am grateful to the organisers of ISBA 2016 to have agreed to carry the burden this year. And to the scientific committee for setting the quality bar that high. (A special thought too for my friend Walter Racugno who had the ultimate bad luck of having an accident the very week of the meeting he had contributed to organise!)

[Even though I predict this is my last post on ISBA 2016 I would be delighted to have guest posts on others’ impressions on the meeting. Feel free to send me entries!]

ISBA 2016 [#6]

Posted in Kids, Mountains, pictures, Statistics, Travel, University life, Wines with tags , , , , , , , , , , , , , , on June 19, 2016 by xi'an

Fifth and final day of ISBA 2016, which was as full and intense as the previous ones. (Or even more if taking into account the late evening social activities pursued by most participants.) First thing in the morning, I managed to get very close to a hill top, thanks to the hints provided by Jeff Miller!, and with no further scratches from the nasty local thorn bushes. And I was back with plenty of time for a Bayesian robustness session with great talks. (Session organised by Judith Rousseau whom I crossed while running, rushing to the airport thanks to an Air France last-minute cancellation.) First talk by James Watson (on his paper with Chris Holmes on Kullback neighbourhoods on priors that Judith and I discussed recently in Statistical Science). Then as a contrapunto Peter Grünwald gave a neat geometric motivation for possible misbehaviour of Bayesian inference in non-convex misspecified environments and discussed his SafeBayes resolution that weights down the likelihood. In a sort of PAC-Bayesian way. And Erlis Ruli presented the ABC-R approach he developed with Laura Ventura and Nicola Sartori based on M-estimators and score functions. Making wonder [idly, as usual] whether cumulating different M-estimators would make a difference in the performances of the ABC algorithm.

David Dunson delivered one of the plenary lectures on high-dimensional discrete parameter estimation, including for instance categorical data. This wide-range talk covered many aspects and papers of David’s work, including a use of tensors I had neither seen nor heard of before before. With sparse modelling to resist the combinatoric explosion of contingency tables. However, and you may blame my Gallic pessimistic daemon for this remark, I have trouble to picture the meaning and relevance of a joint distribution on a space of hundreds and hundreds of dimension and similarly the ability to check the adequacy of any modelling in terms of goodness of fit. For instance, to borrow a non-military example from David’s talk, handling genetic data on ACGT sequences to infer its distribution sounds unreasonable unless most of the bases are mono-allelic. And the only way I see to test the realism of a model in this framework would be to engineer realisations of this distribution to observe the outcome, a test that seems neither feasible not desirable. Prediction based on such models may obviously operate satisfactorily without such realism requirements.

My first afternoon session (after the ISBA assembly that announced the location of ISBA 2020 in Yunnan, China!, home of Pu’ Ehr tea) was about accelerated MCMC schemes with talks by Sanvesh Srivastava on divide-and-conquer MCMC using Wasserstein barycentres, already discussed here, Minsuk Shin on a faster stochastic search variable selection which I could not understand, and Alex Beskos on the extension of Giles’ multilevel Monte Carlo to MCMC settings, which sounded worth investigating further even though I did not follow the notion all the way through. After listening to Luke Bornn explaining how to recalibrate grid data for climate science by accounting for correlation (with the fun title of `lost moments’), I rushed to my rental to [help] cook dinner for friends and… the ISBA 2016 conference was over!

ISBA 2016 [#5]

Posted in Mountains, pictures, Running, Statistics, Travel with tags , , , , , , , , , , , , , on June 18, 2016 by xi'an

from above Forte Village, Santa Magherita di Pula, Sardinia, June 17, 2016On Thursday, I started the day by a rather masochist run to the nearby hills, not only because of the very hour but also because, by following rabbit trails that were not intended for my size, I ended up being scratched by thorns and bramble all over!, but also with neat views of the coast around Pula.  From there, it was all downhill [joke]. The first morning talk I attended was by Paul Fearnhead and about efficient change point estimation (which is an NP hard problem or close to). The method relies on dynamic programming [which reminded me of one of my earliest Pascal codes about optimising a dam debit]. From my spectator’s perspective, I wonder[ed] at easier models, from Lasso optimisation to spline modelling followed by testing equality between bits. Later that morning, James Scott delivered the first Bayarri Lecture, created in honour of our friend Susie who passed away between the previous ISBA meeting and this one. James gave an impressive coverage of regularisation through three complex models, with the [hopefully not degraded by my translation] message that we should [as Bayesians] focus on important parts of those models and use non-Bayesian tools like regularisation. I can understand the practical constraints for doing so, but optimisation leads us away from a Bayesian handling of inference problems, by removing the ascertainment of uncertainty…

Later in the afternoon, I took part in the Bayesian foundations session, discussing the shortcomings of the Bayes factor and suggesting the use of mixtures instead. With rebuttals from [friends in] the audience!

This session also included a talk by Victor Peña and Jim Berger analysing and answering the recent criticisms of the Likelihood principle. I am not sure this answer will convince the critics, but I won’t comment further as I now see the debate as resulting from a vague notion of inference in Birnbaum‘s expression of the principle. Jan Hannig gave another foundation talk introducing fiducial distributions (a.k.a., Fisher’s Bayesian mimicry) but failing to provide a foundational argument for replacing Bayesian modelling. (Obviously, I am definitely prejudiced in this regard.)

The last session of the day was sponsored by BayesComp and saw talks by Natesh Pillai, Pierre Jacob, and Eric Xing. Natesh talked about his paper on accelerated MCMC recently published in JASA. Which surprisingly did not get discussed here, but would definitely deserve to be! As hopefully corrected within a few days, when I recoved from conference burnout!!! Pierre Jacob presented a work we are currently completing with Chris Holmes and Lawrence Murray on modularisation, inspired from the cut problem (as exposed by Plummer at MCMski IV in Chamonix). And Eric Xing spoke about embarrassingly parallel solutions, discussed a while ago here.

ISBA 2016 [#4]

Posted in pictures, Running, Statistics, Travel with tags , , , , , , , , , , on June 17, 2016 by xi'an

As an organiser of the ABC session (along with Paul Fearnhead), I was already aware of most results behind the talks, but nonetheless got some new perspectives from the presentations. Ewan Cameron discussed a two-stage ABC where the first step is actually an indirect inference inference, which leads to a more efficient ABC step. With applications to epidemiology. Lu presented extensions of his work with Paul Fearnhead, incorporating regression correction à la Beaumont to demonstrate consistency and using defensive sampling to control importance sampling variance. (While we are working on a similar approach, I do not want to comment on the consistency part, but I missed how defensive sampling can operate in complex ABC settings, as it requires advanced knowledge on the target to be effective.) And Ted Meeds spoke about two directions for automatising ABC (as in the ABcruise), from incorporating the pseudo-random generator into the representation of the ABC target, to calling for deep learning advances. The inclusion of random generators in the transform is great, provided they can remain black boxes as otherwise they require recoding. (This differs from quasi-Monte Carlo ABC, which aims at reducing the variability due to sheer noise.) It took me a little while, but I eventually understood why Jan Haning saw this inclusion as a return to fiducial inference!

Merlise Clyde gave a wide-ranging plenary talk on (linear) model selection that looked at a large range of priors under the hat of generalised confluent hypergeometric priors over the mixing scale in Zellner’s g-prior. Some were consistent under one or both models, maybe even for misspecified models. Some parts paralleled my own talk on the foundations of Bayesian tests, no wonder since I mostly give a review before launching into a criticism of the Bayes factor. Since I think this may be a more productive perspective than trying to over-come the shortcomings of Bayes factors in weakly informative settings. Some comments at the end of Merlise’s talk were loosely connected to this view in that they called for an unitarian perspective [rather than adapting a prior to a specific inference problem] with decision-theoretic backup. Conveniently the next session was about priors and testing, obviously connected!, with Leo Knorr-Held considering g-priors for the Cox model, Kerrie Mengersen discussing priors for over-fitted mixtures and HMMs, and Dan Simpson entertaining us with his quest of a prior for a point process, eventually reaching PC priors.