Xi'an's Og

With the details from the second speaker:

Adaptive MCMC sampling using a Metropolized PDMP sampler combined with a No-U-Turn criterion

Augustin Chevallier, Université de Strasbourg

Adaptivity in MCMC algorithms is hard to achieve. In Hamiltonian Monte Carlo, for example, it is possible to tune the path length using the No-U-Turn sampler, but the numerical step size cannot be adapted; it can only be tuned. We propose here a new class of algorithm based on Metropolizing a numerical approximation of a PDMP sampler. Like HMC, these samplers require two parameters: a numerical step size and a path length. Unlike HMC, both parameters can be adapted. This paves the way for more robust sampling algorithms, especially for difficult target densities.

This entry was posted on May 22, 2024 at 12:24 am and is filed under Books, Statistics, Travel, University life with tags conditional expectations, free energy, HMC, importance sampling, MCMC, multimodality, Ocean, PariSanté campus, PDMP, piecewise deterministic, reversible jump, seminar, variational autoencoders, Zig-Zag. 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.