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2013 | OriginalPaper | Chapter

3. Bayesian Networks in the Presence of Temporal Information

Authors : Radhakrishnan Nagarajan, Marco Scutari, Sophie Lèbre

Published in: Bayesian Networks in R

Publisher: Springer New York

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Abstract

Real-world entities comprising a complex system evolve as a function of time and respond to external perturbations. Dynamic Bayesian networks extend the fundamental ideas behind static Bayesian networks to model associations arising from the temporal dynamics between the entities of interest. This has to be contrasted with static Bayesian networks, which model the network structure from multiple independent realizations of the entities of a snapshot of the process. More importantly, incorporating the temporal signatures is useful in capturing possible feedback loops that are implicitly disregarded in the case of static Bayesian networks. Since feedback loops are ubiquitous in biological pathways, dynamic Bayesian network modeling is expected to result in better representations of such pathways.
In this chapter, we will introduce basic definitions and models for modeling associations from multivariate linear time series using dynamic Bayesian networks. Applications include modeling gene networks from expression data. Two broad classes of multivariate time series are considered: those whose statistical properties are invariant as a function of time and those whose properties do show change of time.

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previous chapter Bayesian Networks in the Absence of Temporal Information
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Metadata
Title
Bayesian Networks in the Presence of Temporal Information
Authors
Radhakrishnan Nagarajan
Marco Scutari
Sophie Lèbre
Copyright Year
2013
Publisher
Springer New York
Book
Bayesian Networks in R

Print ISBN: 978-1-4614-6445-7

Electronic ISBN: 978-1-4614-6446-4

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-1-4614-6446-4_3

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