PEM-SMC: An algorithm for optimizing model parameters

Bayesian inference is crucial for optimizing parameters in complex models, but often requires sampling due to high-dimensional, intractable posteriors. Beyond Markov-Chain Monte Carlo (MCMC) methods, Sequential Monte Carlo (SMC) algorithms offer an alternative. This paper introduces a Matlab toolbox for the Particle Evolution Metropolis Sequential Monte Carlo (PEM-SMC) algorithm, which combines the strengths of population-based MCMC and SMC. Two case studies – a complex multi-modal probability and a land surface model – demonstrate the toolbox’s capabilities. This tool is valuable for Bayesian inference across fields like statistics, ecology, hydrology, and land surface processes. • The PEM-SMC algorithm is developed for improving sampling diversity and efficiency. • A MATLAB toolbox is developed to broaden the PEM-SMC algorithm’s accessibility. • Practical applications of PEM-SMC are showcased through case studies.

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Publication Details

Journal
Software Impacts
Published
2024-12-13
DOI
https://doi.org/10.1016/j.simpa.2024.100728
Primary Topic
Simulation Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

PEM-SMC: An algorithm for optimizing model parameters

Yunquan Wang, Kun Zhang, Gaofeng Zhu, Wei Gong et al.
Software Impacts
Simulation Techniques and Applications
article

PEM-SMC: An algorithm for optimizing model parameters

Yunquan Wang, Kun Zhang, Gaofeng Zhu, Wei Gong, Xiangyu Yu, Tao Che, Qiang Chen, Cong Xu
article en

Abstract

Bayesian inference is crucial for optimizing parameters in complex models, but often requires sampling due to high-dimensional, intractable posteriors. Beyond Markov-Chain Monte Carlo (MCMC) methods, Sequential Monte Carlo (SMC) algorithms offer an alternative. This paper introduces a Matlab toolbox for the Particle Evolution Metropolis Sequential Monte Carlo (PEM-SMC) algorithm, which combines the strengths of population-based MCMC and SMC. Two case studies – a complex multi-modal probability and a land surface model – demonstrate the toolbox’s capabilities. This tool is valuable for Bayesian inference across fields like statistics, ecology, hydrology, and land surface processes. • The PEM-SMC algorithm is developed for improving sampling diversity and efficiency. • A MATLAB toolbox is developed to broaden the PEM-SMC algorithm’s accessibility. • Practical applications of PEM-SMC are showcased through case studies.

Software ImpactsVol. 23
Sun Yat-sen University (CN), Tianjin University of Finance and Economics (CN), Chinese Academy of Sciences (CN), Beijing Normal University (CN), China University of Geosciences (CN), Northwest Institute of Eco-Environment and Resources (CN), Lanzhou University (CN)
Lanzhou University, National Natural Science Foundation of China, Key Science and Technology Foundation of Gansu Province
Openalex Percentile: Top 72%
Simulation Techniques and Applications
DOIOpen Access PDFOpenAlex
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PEM-SMC: An algorithm for optimizing model parameters — Yunquan Wang, Kun Zhang, et al. · Software Impacts (2024) | TGRS Research Map | TGRS