Generalized parallel tempering on Bayesian inverse problems
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Article
KAUST Department
Applied Mathematics and Computational Science ProgramComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
KAUST Grant Number
OSRURF/1/2281-01-01
URF/1/2584-01-01
Date
2021-08-30Preprint Posting Date
2020-03-06Online Publication Date
2021-08-30Print Publication Date
2021-09Submitted Date
2020-10-05Permanent link to this record
http://hdl.handle.net/10754/662284Metadata
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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.Citation
Latz, J., Madrigal-Cianci, J. P., Nobile, F., & Tempone, R. (2021). Generalized parallel tempering on Bayesian inverse problems. Statistics and Computing, 31(5). doi:10.1007/s11222-021-10042-6Sponsors
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.Open Access funding provided by EPFL Lausanne.
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Springer Science and Business Media LLCJournal
Statistics and ComputingarXiv
2003.03341Additional Links
https://link.springer.com/10.1007/s11222-021-10042-6Scopus Count
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