Multiple projection MCMC algorithms on submanifolds - Archive ouverte HAL Access content directly
Journal Articles IMA Journal of Numerical Analysis Year : 2023

Multiple projection MCMC algorithms on submanifolds

Abstract

We propose new Markov Chain Monte Carlo algorithms to sample probability distributions on submanifolds, which generalize previous methods by allowing the use of set-valued maps in the proposal step of the MCMC algorithms. The motivation for this generalization is that the numerical solvers used to project proposed moves to the submanifold of interest may find several solutions. We show that the new algorithms indeed sample the target probability measure correctly, thanks to some carefully enforced reversibility property. We demonstrate the interest of the new MCMC algorithms on illustrative numerical examples.

Dates and versions

hal-02515267 , version 1 (23-03-2020)

Identifiers

Cite

Tony Lelièvre, Gabriel Stoltz, Wei Zhang. Multiple projection MCMC algorithms on submanifolds. IMA Journal of Numerical Analysis, 2023, 43 (2), pp.737-788. ⟨10.1093/imanum/drac006⟩. ⟨hal-02515267⟩
144 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More