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2018 | OriginalPaper | Chapter

Bayesian and Maximum Entropy Analyses of Flow Networks with Non-Gaussian Priors and Soft Constraints

Authors : Steven H. Waldrip, Robert K. Niven

Published in: Bayesian Inference and Maximum Entropy Methods in Science and Engineering

Publisher: Springer International Publishing

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Abstract

We have recently developed new maximum entropy (MaxEnt) and Bayesian methods for the analysis of flow networks, including pipe flow, electrical and transportation networks. Both methods of inference update a prior probability density function (pdf) with new information, in the form of data or constraints, to obtain a posterior pdf for the system. We here examine the effects of non-Gaussian prior pdfs, including truncated normal and beta distributions, both analytically and by the use of numerical examples, to explore the differences and similarities between the MaxEnt and Bayesian formulations. We also examine ‘soft constraints’ imposed within the prior.

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Title: Bayesian and Maximum Entropy Analyses of Flow Networks with Non-Gaussian Priors and Soft Constraints
Authors: Steven H. Waldrip
Robert K. Niven
Publisher: Springer International Publishing
Book: Bayesian Inference and Maximum Entropy Methods in Science and Engineering
Print ISBN: 978-3-319-91142-7

Electronic ISBN: 978-3-319-91143-4

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-91143-4_27

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