Bayesian and subset-selection methods for parameter estimation in mechanistic models with limited data: A review and comparison

IF 3.7 3区 工程技术 Q2 ENGINEERING, CHEMICAL
Jakob I. Straznicky , Lauren A. Gibson , Benoit Celse , Kimberley B. McAuley
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引用次数: 0

Abstract

Parameters in mathematical models require accurate estimation for the model to give reliable predictions. When data are limited, weighted least-squares methods sometimes result in unreliable parameter estimates. Two popular approaches to combat this issue are subset-selection and Bayesian estimation. Subset-selection ranks model parameters from most- to least-estimable based on prior parameter knowledge and available data. The ranked list is used to determine which parameters should be estimated, and which should be fixed at initial guesses to avoid overfitting. Bayesian estimation methods summarize prior knowledge about parameters using probability distributions. Simple Bayesian methods result in objective functions with penalty terms that keep parameter estimates near their initial guesses unless there is considerable information in the data. Subset-selection and Bayesian methods result in different parameter estimates using the same data and similar prior information. A hydroisomerization case study is presented comparing the merits and shortcoming of each approach. Bayesian estimation is preferred if prior parameter knowledge is reliable, but provides misleading results when the modeler is overly confident about poor parameter guesses. Subset-selection methods are more computationally expensive, less susceptible to problems arising from poor initial guesses, and provide additional information about influences of model parameters and opportunities for model simplification.
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来源期刊
Chemical Engineering Research & Design
Chemical Engineering Research & Design 工程技术-工程:化工
CiteScore
6.10
自引率
7.70%
发文量
623
审稿时长
42 days
期刊介绍: ChERD aims to be the principal international journal for publication of high quality, original papers in chemical engineering. Papers showing how research results can be used in chemical engineering design, and accounts of experimental or theoretical research work bringing new perspectives to established principles, highlighting unsolved problems or indicating directions for future research, are particularly welcome. Contributions that deal with new developments in plant or processes and that can be given quantitative expression are encouraged. The journal is especially interested in papers that extend the boundaries of traditional chemical engineering.
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