Generalized Parallel Tempering for Bayesian Inverse Problems

Juan Pablo Madrigal Cianci (EPFL Lausanne)

May 16. 2022, 11:45 — 12:30

 

In the current work, we present two generalizations of the Parallel Tempering algorithm, inspired by the so-called continuous-time Infinite Swapping algorithm, which found  its origins in the molecular dynamics community, and can be understood as  the continuous-time limit of a Parallel Tempering algorithm with state-dependent swapping rates.  In the current work, we extend this idea to the context of time-discrete Markov chains and present two Markov chain Monte Carlo algorithms that follow the same paradigm as the   continuous-time infinite swapping procedure. We present results on the reversibility and ergodicity properties of our generalized PT algorithms. Numerical results on sampling from different target distributions originating from Bayesian inverse problems, show that the proposed methods significantly improve sampling efficiency over more traditional sampling algorithms such as Random Walk Metropolis and  (standard)  Parallel Tempering.

Further Information
Venue:
ESI Boltzmann Lecture Hall
Recordings:
Recording
Associated Event:
Computational Uncertainty Quantification: Mathematical Foundations, Methodology & Data (Thematic Programme)
Organizer(s):
Clemens Heitzinger (TU Vienna)
Fabio Nobile (EPFL Lausanne)
Robert Scheichl (U Heidelberg)
Christoph Schwab (ETH Zürich)
Sara van de Geer (ETH Zürich)
Karen Willcox (U of Texas, Austin)