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.

a film about Stan [not a film review]

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

scale matters [maths as well]

A question from X validated on why an independent Metropolis sampler of a three component Normal mixture based on a single Normal proposal was failing to recover the said mixture…

When looking at the OP’s R code, I did not notice anything amiss at first glance (I was about to drive back from Annecy, hence did not look too closely) and reran the attached code with a larger variance in the proposal, which returned the above picture for the MCMC sample, close enough (?) to the target. Later, from home, I checked the code further and noticed that the Metropolis ratio was only using the ratio of the targets. Dividing by the ratio of the proposals made a significant (?) to the representation of the target.

More interestingly, the OP was fundamentally confused between independent and random-walk Rosenbluth algorithms, from using the wrong ratio to aiming at the wrong scale factor and average acceptance ratio, and furthermore challenged by the very notion of Hessian matrix, which is often suggested as a default scale.