PARALLEL ADAPTIVE MULTILEVEL SAMPLING ALGORITHMS FOR THE BAYESIAN ANALYSIS OF MATHEMATICAL MODELS
Permanent link to this recordhttp://hdl.handle.net/10754/599135
MetadataShow full item record
AbstractIn recent years, Bayesian model updating techniques based on measured data have been applied to many engineering and applied science problems. At the same time, parallel computational platforms are becoming increasingly more powerful and are being used more frequently by the engineering and scientific communities. Bayesian techniques usually require the evaluation of multi-dimensional integrals related to the posterior probability density function (PDF) of uncertain model parameters. The fact that such integrals cannot be computed analytically motivates the research of stochastic simulation methods for sampling posterior PDFs. One such algorithm is the adaptive multilevel stochastic simulation algorithm (AMSSA). In this paper we discuss the parallelization of AMSSA, formulating the necessary load balancing step as a binary integer programming problem. We present a variety of results showing the effectiveness of load balancing on the overall performance of AMSSA in a parallel computational environment.
CitationPrudencio E, Cheung SH (2012) PARALLEL ADAPTIVE MULTILEVEL SAMPLING ALGORITHMS FOR THE BAYESIAN ANALYSIS OF MATHEMATICAL MODELS. International Journal for Uncertainty Quantification 2: 215–237. Available: http://dx.doi.org/10.1615/Int.J.UncertaintyQuantification.2011003499.
SponsorsThis research was supported by the Department of Energy (National Nuclear Security Administration), under the Predictive Science Academic Alliance Program (PSAAP), Award Number DE-FC52-08NA28615, and by the King Abdullah University of Science and Technology (KAUST), under the Academic Excellence Alliance (AEA) program. E.E.P. was also partially supported by Sandia National Laboratories under Contract Numbers 1017123 and 1086312. All calculations were performed on the RANGER high-performance computer at the Texas Advanced Computing Center (TACC) . The authors are also thankful to two anonymous referees, whose comments and questions helped them to improve their paper.