Multilevel sequential Monte Carlo samplers
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:

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 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:
 Grant/Contract Number:
 AC0500OR22725; R155000143112
 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
 Research Org:
 Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
 Sponsoring Org:
 USDOE Laboratory Directed Research and Development (LDRD) Program
 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
 OSTI Identifier:
 1302922