Xi'an's Og

At the last mostly Monte Carlo seminar, Pierre Monmarché presented a recent work on post-sampling for multimodal targets: while I  consider the main problem in sampling from generic multimodal targets stands with finding the modes, rather than with exploring local aspects or estimating relative weights of said modes, this made me ponder whether or not this could be accelerated by removing chunks of the already explored modes to induce moves elsewhere, which is a form of radical, brute-force, tempering, or of Wang-Landau.  As for the relative weights, a multiple move proposal can be considered, including our folding idea. Or Geyer’s inverse logistic trick. Or the similar mixture trick we used in our Biometrika paper on nested sampling. Pierre’s approach was closer to adaptive importance sampling, with a self-imposed constraint of fixed sample sizes from (approximate) distributions around each of the modes.

This entry was posted on April 30, 2026 at 12:26 am and is filed under Books, Statistics, University life with tags adaptive importance sampling, Biometrika, Charlie Geyer, inverse probability weight, mostly Monte Carlo seminar, multimodal target, nested sampling, post-processing, simulated tempering, Université Gustave Eiffel, Wang-Landau algorithm. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.