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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Acknowledgements
This project acknowledges funding support from the Australian Research Council Discovery Projects Grant DP140104402, Go8/DAAD Australia-Germany Joint Research Cooperation Scheme RG123832 and the French Agence Nationale de la Recherche Chair of Excellence (TUCOROM) and the Institute Prime, Poitiers, France.
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Waldrip, S.H., Niven, R.K. (2018). Bayesian and Maximum Entropy Analyses of Flow Networks with Non-Gaussian Priors and Soft Constraints. In: Polpo, A., Stern, J., Louzada, F., Izbicki, R., Takada, H. (eds) Bayesian Inference and Maximum Entropy Methods in Science and Engineering. maxent 2017. Springer Proceedings in Mathematics & Statistics, vol 239. Springer, Cham. https://doi.org/10.1007/978-3-319-91143-4_27
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DOI: https://doi.org/10.1007/978-3-319-91143-4_27
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