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

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.