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: