Generalized parallel tempering on Bayesian inverse problems

Jonas Latz, Juan P. Madrigal-Cianci, Fabio Nobile, Raul Tempone

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


AbstractIn the current work we present two generalizations of the Parallel Tempering algorithm in the context of discrete-time Markov chain Monte Carlo methods for Bayesian inverse problems. These generalizations use state-dependent swapping rates, inspired by the so-called continuous time Infinite Swapping algorithm presented in Plattner et al. (J Chem Phys 135(13):134111, 2011). We analyze the reversibility and ergodicity properties of our generalized PT algorithms. Numerical results on sampling from different target distributions, show that the proposed methods significantly improve sampling efficiency over more traditional sampling algorithms such as Random Walk Metropolis, preconditioned Crank–Nicolson, and (standard) Parallel Tempering.
Original languageEnglish (US)
JournalStatistics and Computing
Issue number5
StatePublished - Aug 30 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-09-01
Acknowledged KAUST grant number(s): OSR, URF/1/2281-01-01, URF/1/2584-01-01
Acknowledgements: We would like to thank the anonymous reviewers for helpful suggestions that significantly improved this work.This publication was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under award numbers URF/1/2281-01-01 and URF/1/2584-01-01 in the KAUST Competitive Research Grants Programs- Round 3 and 4, respectively, and the Alexander von Humboldt Foundation. Jonas Latz acknowledges support by the Deutsche Forschungsgemeinschaft (DFG) through the TUM International Graduate School of Science and Engineering (IGSSE) within the project 10.02 BAYES. Juan P. Madrigal-Cianci and Fabio Nobile also acknowledge support from the Swiss Data Science Center (SDSC) Grant p18-09.

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science
  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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