Blackwell-Rosenbluth awards 2022

Here are the Winners of the j-ISBA Blackwell-Rosenbluth awards 2022, between those based on the time zones UTC-12 to UTC-1 (aka the Americas):

• Sharmistha Guha, Texas A&M University
  Simon Mak, Duke University
  Akihiko Nishimura, Johns Hopkins University

and those based on the time zones UTC+0 to UTC+13 (aka the Americas^c):

• Jeremy Heng, ESSEC Business School, Singapore
  Swapnil Mishra, University of Copenhagen
  Leah South, Queensland University of Technology

Congrats!!! They will all present their webinar on 28 or 29 November at 1pm UTC (Universal Time Coordinate).

Blackwell-Rosenbluth Award deadline extended to 7 August 2022

The deadline for submission of a nomination for the Blackwell-Rosenbluth j-ISBA Award is now 7 August. Ph.D. students or early career researchers who obtained their PhD after January 1, 2017 are eligible for nomination. A nomination may come from any ISBA member, including the nominee themselves.

Blackwell-Rosenbluth Awards 2021

Congratulations to the winners of the newly created award! This j-ISBA award is intended for junior researchers in different areas of Bayesian statistics. And named after David Blackwell and Arianna  Rosenbluth. They will present their work at the newly created JB³ seminars on 10 and 12 November, both at 1pm UTC. (The awards are broken into two time zones, corresponding to the Americas and the rest of the World.)

UTC+0 to UTC+13

Marta Catalano, Warwick University
Samuel Livingstone, University College London
Dootika Vats, Indian Institute of Technology Kanpur

UTC-12 to UTC-1

Trevor Campbell, University of British Columbia
Daniel Kowal, Rice University
Yixin Wang, University of Michigan

Rao-Blackwellisation in the MCMC era

A few months ago, as indicated on this blog, I was contacted by ISR editors to write a piece on Rao-Blackwellisation, towards a special issue celebrating Calyampudi Radhakrishna Rao’s 100th birthday. Gareth Roberts and I came up with this survey, now on arXiv, discussing different aspects of Monte Carlo and Markov Chain Monte Carlo that pertained to Rao-Blackwellisation, one way or another. As I discussed the topic with several friends over the Fall, it appeared that the difficulty was more in setting the boundaries. Than in finding connections. In a way anything conditioning or demarginalising or resorting to auxiliary variates is a form of Rao-Blackwellisation. When re-reading the JASA Gelfand and Smith 1990 paper where I first saw the link between the Rao-Blackwell theorem and simulation, I realised my memory of it had drifted from the original, since the authors proposed there an approximation of the marginal based on replicas rather than the original Markov chain. Being much closer to Tanner and Wong (1987) than I thought. It is only later that the true notion took shape. [Since the current version is still a draft, any comment or suggestion would be most welcomed!]

Rao-Blackwellisation, a review in the making

Recently, I have been contacted by a mainstream statistics journal to write a review of Rao-Blackwellisation techniques in computational statistics, in connection with an issue celebrating C.R. Rao’s 100th birthday. As many many techniques can be interpreted as weak forms of Rao-Blackwellisation, as e.g. all auxiliary variable approaches, I am clearly facing an abundance of riches and would thus welcome suggestions from Og’s readers on the major advances in Monte Carlo methods that can be connected with the Rao-Blackwell-Kolmogorov theorem. (On the personal and anecdotal side, I only met C.R. Rao once, in 1988, when he came for a seminar at Purdue University where I was spending the year.)