Multilevel sequential Monte Carlo samplers
Abstract
Here, we study the approximation of expectations w.r.t. probability distributions associated to the solution of partial differential equations (PDEs); this scenario appears routinely in Bayesian inverse problems. In practice, one often has to solve the associated PDE numerically, using, for instance finite element methods and leading to a discretisation bias, with the stepsize level h _{L}. In addition, the expectation cannot be computed analytically and one often resorts to Monte Carlo methods. In the context of this problem, it is known that the introduction of the multilevel Monte Carlo (MLMC) method can reduce the amount of computational effort to estimate expectations, for a given level of error. This is achieved via a telescoping identity associated to a Monte Carlo approximation of a sequence of probability distributions with discretisation levels $${\infty}$$ >h _{0}>h _{1 }...>h _{L}. In many practical problems of interest, one cannot achieve an i.i.d. sampling of the associated sequence of probability distributions. A sequential Monte Carlo (SMC) version of the MLMC method is introduced to deal with this problem. In conclusion, it is shown that under appropriate assumptions, the attractive property of a reduction of the amount of computational effort to estimate expectations, for a given level of error, can be maintained within the SMC context.
 Authors:

 Univ. College London, London (United Kingdom). Dept. of Statistical Science
 National Univ. of Singapore (Singapore). Dept. of Statistics & Applied Probability
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division
 King Abdullah Univ. of Science and Technology, Thuwal (Saudi Arabia)
 Publication Date:
 Research Org.:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Sponsoring Org.:
 USDOE Laboratory Directed Research and Development (LDRD) Program
 OSTI Identifier:
 1302922
 Grant/Contract Number:
 AC0500OR22725; R155000143112
 Resource Type:
 Accepted Manuscript
 Journal Name:
 Stochastic Processes and Their Applications
 Additional Journal Information:
 Journal Name: Stochastic Processes and Their Applications; Journal ID: ISSN 03044149
 Publisher:
 Elsevier
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING; 77 NANOSCIENCE AND NANOTECHNOLOGY; multilevel Monte Carlo; sequential Monte Carlo; Bayesian inverse problems
Citation Formats
Beskos, Alexandros, Jasra, Ajay, Law, Kody, Tempone, Raul, and Zhou, Yan. Multilevel sequential Monte Carlo samplers. United States: N. p., 2016.
Web. doi:10.1016/j.spa.2016.08.004.
Beskos, Alexandros, Jasra, Ajay, Law, Kody, Tempone, Raul, & Zhou, Yan. Multilevel sequential Monte Carlo samplers. United States. doi:10.1016/j.spa.2016.08.004.
Beskos, Alexandros, Jasra, Ajay, Law, Kody, Tempone, Raul, and Zhou, Yan. Wed .
"Multilevel sequential Monte Carlo samplers". United States. doi:10.1016/j.spa.2016.08.004. https://www.osti.gov/servlets/purl/1302922.
@article{osti_1302922,
title = {Multilevel sequential Monte Carlo samplers},
author = {Beskos, Alexandros and Jasra, Ajay and Law, Kody and Tempone, Raul and Zhou, Yan},
abstractNote = {Here, we study the approximation of expectations w.r.t. probability distributions associated to the solution of partial differential equations (PDEs); this scenario appears routinely in Bayesian inverse problems. In practice, one often has to solve the associated PDE numerically, using, for instance finite element methods and leading to a discretisation bias, with the stepsize level hL. In addition, the expectation cannot be computed analytically and one often resorts to Monte Carlo methods. In the context of this problem, it is known that the introduction of the multilevel Monte Carlo (MLMC) method can reduce the amount of computational effort to estimate expectations, for a given level of error. This is achieved via a telescoping identity associated to a Monte Carlo approximation of a sequence of probability distributions with discretisation levels ${\infty}$ >h0>h1 ...>hL. In many practical problems of interest, one cannot achieve an i.i.d. sampling of the associated sequence of probability distributions. A sequential Monte Carlo (SMC) version of the MLMC method is introduced to deal with this problem. In conclusion, it is shown that under appropriate assumptions, the attractive property of a reduction of the amount of computational effort to estimate expectations, for a given level of error, can be maintained within the SMC context.},
doi = {10.1016/j.spa.2016.08.004},
journal = {Stochastic Processes and Their Applications},
number = ,
volume = ,
place = {United States},
year = {2016},
month = {8}
}
Web of Science
Works referencing / citing this record:
A transportbased multifidelity preconditioner for Markov chain Monte Carlo
journal, November 2019
 Peherstorfer, Benjamin; Marzouk, Youssef
 Advances in Computational Mathematics
A transportbased multifidelity preconditioner for Markov chain Monte Carlo
journal, November 2019
 Peherstorfer, Benjamin; Marzouk, Youssef
 Advances in Computational Mathematics