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System Modeling in Cellular BiologyFrom Concepts to Nuts and Bolts$
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Zoltan Szallasi, Jorg Stelling, and Vipul Periwal

Print publication date: 2006

Print ISBN-13: 9780262195485

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262195485.001.0001

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Bayesian Inference of Biological Systems: The Logic of Biology

Bayesian Inference of Biological Systems: The Logic of Biology

Chapter:
(p.53) 4. Bayesian Inference of Biological Systems: The Logic of Biology
Source:
System Modeling in Cellular Biology
Author(s):

Vipul Periwal

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262195485.003.0004

Systematic model selection and inference in modeling biological systems must deal with the specific problems of incomplete prior knowledge, limited heterogeneous data, and similar but not identical model systems. The model selection process must also allow incremental updates as new data becomes available. Probability theory as embodied in Bayes’ theorem is the unique logically consistent framework for such reasoning. This chapter summarizes the foundations of Bayesian inference along with some excursions into information theory and search theory. It also reviews some recent examples taken from the recent literature.

Keywords:   systems biology, model selection, Bayes’ theorem, probability theory, information theory, search theory

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