An introduction to Bayesian methods for analyzing chemistry data: Part II: A review of applications of Bayesian methods in chemistry

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• 作者：D.B. Hibbert ; N. Armstrong
• 关键词：Bayesian analysis ; Chemistry data analysis ; Chemometrics and statistics ; Forensic sciences and medical testing ; Model selection ; Parameter estimation
• 刊名：Chemometrics and Intelligent Laboratory Systems
• 出版年：2009
• 出版时间：15 July 2009
• 年：2009
• 卷：97
• 期：2
• 页码：211-220
• 全文大小：310 K

文摘

A critical literature review with 207 references is presented on the use of Bayes theorem in chemistry. Discussion is grouped into areas of application, including general chemistry, chromatography and mass spectrometry, spectroscopy, microbiology, and metrology in chemistry and environmental chemistry. Reference to methodology is given to Part I of this series. Recurring themes throughout chemistry are parameter estimation (often using marginalization), joint distributions calculated by Markov Chain Monte Carlo methods, Bayesian classification, Bayesian regularized artificial neural networks, and the use of Bayesian priors to incorporate expert knowledge.