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Computational Toxicology pp 597–636Cite as

Bayesian Inference

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 930))

Abstract

This chapter provides an overview of the Bayesian approach to data analysis, modeling, and statistical decision making. The topics covered go from basic concepts and definitions (random variables, Bayes’ rule, prior distributions) to various models of general use in biology (hierarchical models, in particular) and ways to calibrate and use them (MCMC methods, model checking, inference, and decision). The second half of this Bayesian primer develops an example of model setup, calibration, and inference for a physiologically based analysis of 1,3-butadiene toxicokinetics in humans.

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Notes

  1. 1.

    Note that if the data were exactly what we expected a priori, there would be not much need to improve the model.

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Author information

Authors and Affiliations

  1. Royallieu Research Center, Technological University of Compiegne, Compiegne, France

    Frederic Y. Bois

  2. INERIS, DRC/VIVA/METO, Verneuil en Halatte, France

    Frederic Y. Bois

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  1. Frederic Y. Bois
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Correspondence to Frederic Y. Bois .

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Editors and Affiliations

  1. School of Biomedical Engineering, Chemical & Biological Engineering, Colorado State University, Campus Delivery 1370, Fort Collins, 80523-1370, Colorado, USA

    Brad Reisfeld

  2. , Chemical & Biological Engineering, Colorado State University, Campus Delivery 1370, Fort Collins, 80523-1370, Colorado, USA

    Arthur N. Mayeno

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Bois, F.Y. (2013). Bayesian Inference. In: Reisfeld, B., Mayeno, A. (eds) Computational Toxicology. Methods in Molecular Biology, vol 930. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-059-5_25

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  • DOI: https://doi.org/10.1007/978-1-62703-059-5_25

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  • Publisher Name: Humana Press, Totowa, NJ

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