TY - JOUR

T1 - Sequential Monte Carlo methods for Bayesian elliptic inverse problems

AU - Beskos, Alexandros

AU - Jasra, Ajay

AU - Muzaffer, Ege A.

AU - Stuart, Andrew M.

N1 - Generated from Scopus record by KAUST IRTS on 2019-11-20

PY - 2015/7/26

Y1 - 2015/7/26

N2 - In this article, we consider a Bayesian inverse problem associated to elliptic partial differential equations in two and three dimensions. This class of inverse problems is important in applications such as hydrology, but the complexity of the link function between unknown field and measurements can make it difficult to draw inference from the associated posterior. We prove that for this inverse problem a basic sequential Monte Carlo (SMC) method has a Monte Carlo rate of convergence with constants which are independent of the dimension of the discretization of the problem; indeed convergence of the SMC method is established in a function space setting. We also develop an enhancement of the SMC methods for inverse problems which were introduced in Kantas et al. (SIAM/ASA J Uncertain Quantif 2:464–489, 2014); the enhancement is designed to deal with the additional complexity of this elliptic inverse problem. The efficacy of the methodology and its desirable theoretical properties, are demonstrated for numerical examples in both two and three dimensions.

AB - In this article, we consider a Bayesian inverse problem associated to elliptic partial differential equations in two and three dimensions. This class of inverse problems is important in applications such as hydrology, but the complexity of the link function between unknown field and measurements can make it difficult to draw inference from the associated posterior. We prove that for this inverse problem a basic sequential Monte Carlo (SMC) method has a Monte Carlo rate of convergence with constants which are independent of the dimension of the discretization of the problem; indeed convergence of the SMC method is established in a function space setting. We also develop an enhancement of the SMC methods for inverse problems which were introduced in Kantas et al. (SIAM/ASA J Uncertain Quantif 2:464–489, 2014); the enhancement is designed to deal with the additional complexity of this elliptic inverse problem. The efficacy of the methodology and its desirable theoretical properties, are demonstrated for numerical examples in both two and three dimensions.

UR - http://link.springer.com/10.1007/s11222-015-9556-7

UR - http://www.scopus.com/inward/record.url?scp=84932194812&partnerID=8YFLogxK

U2 - 10.1007/s11222-015-9556-7

DO - 10.1007/s11222-015-9556-7

M3 - Article

VL - 25

JO - Statistics and Computing

JF - Statistics and Computing

SN - 1573-1375

IS - 4

ER -