图书馆订阅: Guest
Begell Digital Portal Begell 数字图书馆 电子图书 期刊 参考文献及会议录 研究收集
国际不确定性的量化期刊
影响因子: 4.911 5年影响因子: 3.179 SJR: 1.008 SNIP: 0.983 CiteScore™: 5.2

ISSN 打印: 2152-5080
ISSN 在线: 2152-5099

Open Access

国际不确定性的量化期刊

DOI: 10.1615/Int.J.UncertaintyQuantification.2011003499
pages 215-237

PARALLEL ADAPTIVE MULTILEVEL SAMPLING ALGORITHMS FOR THE BAYESIAN ANALYSIS OF MATHEMATICAL MODELS

Ernesto Prudencio
Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, Texas, USA
Sai Hung Cheung
School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore

ABSTRACT

In 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.


Articles with similar content:

FORWARD AND INVERSE UNCERTAINTY QUANTIFICATION USING MULTILEVEL MONTE CARLO ALGORITHMS FOR AN ELLIPTIC NONLOCAL EQUATION
International Journal for Uncertainty Quantification, Vol.6, 2016, issue 6
Ajay Jasra, Yan Zhou, Kody J.H. Law
POLYNOMIAL-CHAOS-BASED KRIGING
International Journal for Uncertainty Quantification, Vol.5, 2015, issue 2
Joe Wiart, Bruno Sudret, Roland Schobi
STOCHASTIC DESIGN AND CONTROL IN RANDOM HETEROGENEOUS MATERIALS
International Journal for Multiscale Computational Engineering, Vol.9, 2011, issue 4
Phaedon-Stelios Koutsourelakis, Raphael Sternfels
DATA-CONSISTENT SOLUTIONS TO STOCHASTIC INVERSE PROBLEMS USING A PROBABILISTIC MULTI-FIDELITY METHOD BASED ON CONDITIONAL DENSITIES
International Journal for Uncertainty Quantification, Vol.10, 2020, issue 5
L. Bruder, Timothy Wildey, M. W. Gee
ASYMPTOTICALLY INDEPENDENT MARKOV SAMPLING: A NEW MARKOV CHAIN MONTE CARLO SCHEME FOR BAYESIAN INFERENCE
International Journal for Uncertainty Quantification, Vol.3, 2013, issue 5
James L. Beck, Konstantin M. Zuev