Abstract
This chapter presents a review of the causal networks (i.e., the Bayesian networks) which is a probabilistic directed acyclic graphical model. In this thesis, we use the causal networks to describe the complex nonequilibrium stochastic dynamics. We show examples of the causal networks for several physical situations such as the Markov chain, the feedback control and the coupled Langevin equations.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Morgan Kaufmann, San Mateo, 1988)
J. Pearl, Causality: Models, Reasoning and Inference (MIT press, Cambridge, 2000)
F.V. Jensen, T.D. Nielsen, Bayesian Networks and Decision Graphs (Springer, Berlin, 2009)
M. Minsky, Steps toward artificial intelligence. Comput. Thought 406, 450 (1963)
J. Pearl, Fusion, propagation, and structuring in belief networks. Artif. Intell. 29, 241 (1986)
C.M. Bishop, Pattern Recognition and Machine Learning (Springer, New York, 2006)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this chapter
Cite this chapter
Ito, S. (2016). Bayesian Networks and Causal Networks. In: Information Thermodynamics on Causal Networks and its Application to Biochemical Signal Transduction. Springer Theses. Springer, Singapore. https://doi.org/10.1007/978-981-10-1664-6_5
Download citation
DOI: https://doi.org/10.1007/978-981-10-1664-6_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-1662-2
Online ISBN: 978-981-10-1664-6
eBook Packages: Physics and AstronomyPhysics and Astronomy (R0)