ISBA regional meeting in Varanasi (day 3)

On the last/my day of the ISBA meeting in Varanasi, I attended a few talks before being kindly driven to the airport (early, too early, but with the unpredictable traffic there, it was better to err on the cautionary side!). In the dynamical model session, Simon Wilson presented a way to approximate posteriors for HMMs based on Chib’s (or Bayes’!) formula, while Jonathan Stroud exposed another approach to state-space model approximation involving a move of the state parameter based on a normal approximation of its conditional given the observable, approximation which seemed acceptable for the cloud analysis model he was processing. Nicolas Chopin then gave a quick introduction to particle MCMC, all the way to SMC². (As a stern chairmain of the session, I know Nicolas felt he did not have enough time but he did a really good job of motivating those different methods, in particular in explaining why the auxiliary variable approach makes the unbiased estimator of the likelihood a valid MCMC method.) Peter Green’s plenary talk was about a emission tomography image analysis whose statistical processing turned into a complex (Bernstein-von Mises) convergence theorem (whose preliminary version I saw in Bristol during Natalia Bochkina’s talk).

Overall, as forewarned by and expected from the program, this ISBA meeting was of the highest scientific quality. (I only wish I had had hindi god abilities to duplicate and attend several parallel sessions at the same time!) Besides, much besides!, the wamr attention paid to everyone by the organisers was just simply un-be-lie-vable! The cultural program went in par with the scientific program. The numerous graduate students and faculty involved in the workshop organisation had a minute knowledge of our schedules and locations, and were constantly anticipating our needs and moves. Almost to a fault, i.e. to a point that was close to embarassing for our cultural habits. I am therefore immensely grateful [personally and as former ISBA president] to all those people that contributed to the success of this ISBA meeting and first and foremost to Professor Satyanshu Upadhyay who worked relentlessly towards this goal during many months! (As a conference organiser, I realise I was and am simply unable to provide this level of welcome to the participants, even for much smaller meetings… The contrast with my previous conference in Berlin could not be more extreme as, for a much higher registration fee, the return was very, very limited.) I will forever (at least until my next reincarnation!) keep the memory of this meeting as a very special one, quite besides giving me the opportunity of my first visit to India…

ISBA Regional Meeting in Varanasi (day 2)

A second full day at the ISBA meeting in Varanasi: I attended a non-parametric session with Sonia Petrone talking about mixtures of regressions (more precisely, piecewise linear functions) and Ramses Mena defining stationary processes via a Gibbs-like construction (which I would have liked to have more time to fully understand). Then Jamie Robbins gave a talk related to the paradox raised by Robbins and Ritov and discussed recently by Chris Sims. (Jamie asked for my opinion at the end of the talk, but I had none, considering the problem to be more of an epiphenomenon than a genuine statistical difficulty… I may comment more on this question later, almost feel compelled to by Jamie’s interpelation, but I had not much to say at this stage! It sounds like another of those infinite dimensional problems where the Bayesian solution can get stranded.) I then attended Murray Aitkin’s talk, where he reanalysed the Berkof et al. (2003) dataset using his integrated likelihood. The afternoon was a succession of plenary talks by Susie Bayarri, Fabrizio Ruggeri and Peter Muller. (It could have been called the afternoon of the ISBA past-presidents, as I also talked in this series!) Susie introduced a new notion of effective sample size, call TESS, not in the importance sampling sense of independent-sample-equivalent used in simulation, but in the model comparison sense of information criterion penalising and prior scaling factor. This was the first time I heard about this notion and I found it definitely worth pursuing, in particular in search of a connection with the g-prior. (Nice name too!, connecting to a great book with a quote from Hardy about Tess being the victim of her beauty…) The day ended with a group excursion on boats up the Ganges for attending the sunset (Ganga Aarti, आरती) ceremony at Dasaswamedh Ghat, a ceremony that remained rather esoteric [for me] without the proper explanation.

ISBA Regional Meeting in Varanasi

So, after one week of travelling around India and posting only pictures, a post about the initial reason I came to India! The meeting started on Monday, yesterday, at the Banaras Hindu University, in Varanasi. I was first amazed by the large number of participants, around 350, until I realised there were more than 50 students in the BHU Stat department alone. The opening ceremony was more formal than usual, with many welcoming talks, and even had a religious component with songs and flower necklaces around the bust of the University founder. After this ceremony, Jim Berger gave a general public talk on the dangers of p-values and multiple testing, worth repeating on a regular basis. Then Nozer Singpurwalla presented a foundational lecture aiming at replacing probability by prevalence in reliability, lecture that would certainly appeal to Krzysztof Burdzy as it mostly dealt with the early works on the formalisation of probability. I had to skip John Geweke’s talk on fast Monte Carlo methods, alas, as I needed to go and buy a down jacket to fight the so-unusual cold wave over Northern India in general and Varanasi in particular, where heating is unheard of… Today, I mostly attended MCMC-related talks, including a presentation by Vivek Roy of the technique he had discussed with me two months ago in Ames. The idea is quite interesting if maybe impractical: the ergodic theorem does not require the stationary measure to be proper for averages to converge (provided the function is integrable). Thus one can run a Markov chain to approximate integrals against an improper measure that is the stationary measure of this chain. I alas missed most of Adam Johansen’s talk on the Rao-Blackwellisation of Monte Carlo, as I could not but doze, thanks to a sleepless night fighting both the cold and internal disruptions… The day also saw interesting plenary sessions by Tony O’Hagan on computer experiments (with the obligated barb on Objective Bayes), Jayanta Ghosh on clustering as a non-parametric method (which made me ponder whether a Dirichlet process version of the empirical likelihood approximation was available), and Robert Kohn on upper bounds on the inefficiency of an unbiased estimator of the target distribution. (No picture of Varanasi today, as my [new] hotel wireless does not like transfers!) Here are the slides of my talk tomorrow, rewriting the Bristol talk with emphasis on empirical likelihood: