A few days ago I found on the page Jeff Rosenthal has dedicated to Hastings that he has passed away peacefully on May 13, 2016 in Victoria, British Columbia, where he lived for 45 years as a professor at the University of Victoria. After holding positions at University of Toronto, University of Canterbury (New Zealand), and Bell Labs (New Jersey). As pointed out by Jeff, Hastings’ main paper is his 1970 Biometrika description of Markov chain Monte Carlo methods, Monte Carlo sampling methods using Markov chains and their applications. Which would take close to twenty years to become known to the statistics world at large, although you can trace a path through Peskun (his only PhD student) , Besag and others. I am sorry it took so long to come to my knowledge and also sorry it apparently went unnoticed by most of the computational statistics community.
Archive for Canada
Next summer of 2017, the biennial International Conference on Monte Carlo Methods and Applications (MCM) will take place in Montréal, Québec, Canada, on July 3-7. This is a mathematically-oriented meeting that works in alternance with MCqMC and that is “devoted to the study of stochastic simulation and Monte Carlo methods in general, from the theoretical viewpoint and in terms of their effective applications in different areas such as finance, statistics, machine learning, computer graphics, computational physics, biology, chemistry, and scientific computing in general. It is one of the most prominent conference series devoted to research on the mathematical aspects of stochastic simulation and Monte Carlo methods.” I attended one edition in Annecy three years ago and enjoyed very much the range of topics and backgrounds. The program is under construction and everyone is warmly invited to contribute talks or special sessions, with a deadline on January 20, 2017. In addition, Montréal is a Monte Carlo Mecca of sorts with leading researchers in the field like Luc Devroye and Pierre Lécuyer working there. (And a great place to visit in the summer!)
My friends from Toronto Radu Craiu and Jeff Rosenthal have arXived a paper along with Reihaneh Entezari on MCMC scaling for large datasets, in the spirit of Scott et al.’s (2013) consensus Monte Carlo. They devised an likelihood inflated algorithm that brings a novel perspective to the problem of large datasets. This question relates to earlier approaches like consensus Monte Carlo, but also kernel and Weierstrass subsampling, already discussed on this blog, as well as current research I am conducting with my PhD student Changye Wu. The approach by Entezari et al. is somewhat similar to consensus Monte Carlo and the other solutions in that they consider an inflated (i.e., one taken to the right power) likelihood based on a subsample, with the full sample being recovered by importance sampling. Somewhat unsurprisingly this approach leads to a less dispersed estimator than consensus Monte Carlo (Theorem 1). And the paper only draws a comparison with that sub-sampling method, rather than covering other approaches to the problem, maybe because this is the most natural connection, one approach being the k-th power of the other approach.
“…we will show that [importance sampling] is unnecessary in many instances…” (p.6)
An obvious question that stems from the approach is the call for importance sampling, since the numerator of the importance sampler involves the full likelihood which is unavailable in most instances when sub-sampled MCMC is required. I may have missed the part of the paper where the above statement is discussed, but the only realistic example discussed therein is the Bayesian regression tree (BART) of Chipman et al. (1998). Which indeed constitutes a challenging if one-dimensional example, but also one that requires delicate tuning that leads to cancelling importance weights but which may prove delicate to extrapolate to other models.
Just a reminder about the incoming deadline of January 31 for the CANSSI/INCASS postdoctoral grants. Those are restricted to members of CANSSI member institutions, though:
For the 2016-2017 year, up to two CANSSI Postdoctoral Fellowships will be awarded to suitable statistical sciences candidates working in CANSSI member institutions. Candidates should have received their PhD after March 31, 2013, and must have fulfilled all PhD requirements by the time of taking up the award.