Sum-product laws and efficient algorithms for imprecise Markov chains

Jasper De Bock, Alexander Erreygers, Thomas Krak
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:1476-1485, 2021.

Abstract

We propose two sum-product laws for imprecise Markov chains, and use these laws to derive two algorithms to efficiently compute lower and upper expectations for imprecise Markov chains under complete independence and epistemic irrelevance. These algorithms work for inferences that have a corresponding sum-product decomposition, and we argue that many well-known inferences fit their scope. We illustrate our results on a simple epidemiological example.

Cite this Paper

BibTeX
@InProceedings{pmlr-v161-de-bock21a, title = {Sum-product laws and efficient algorithms for imprecise Markov chains}, author = {De Bock, Jasper and Erreygers, Alexander and Krak, Thomas}, booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence}, pages = {1476--1485}, year = {2021}, editor = {de Campos, Cassio and Maathuis, Marloes H.}, volume = {161}, series = {Proceedings of Machine Learning Research}, month = {27--30 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v161/de-bock21a/de-bock21a.pdf}, url = {https://proceedings.mlr.press/v161/de-bock21a.html}, abstract = {We propose two sum-product laws for imprecise Markov chains, and use these laws to derive two algorithms to efficiently compute lower and upper expectations for imprecise Markov chains under complete independence and epistemic irrelevance. These algorithms work for inferences that have a corresponding sum-product decomposition, and we argue that many well-known inferences fit their scope. We illustrate our results on a simple epidemiological example.} }
Endnote
%0 Conference Paper %T Sum-product laws and efficient algorithms for imprecise Markov chains %A Jasper De Bock %A Alexander Erreygers %A Thomas Krak %B Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2021 %E Cassio de Campos %E Marloes H. Maathuis %F pmlr-v161-de-bock21a %I PMLR %P 1476--1485 %U https://proceedings.mlr.press/v161/de-bock21a.html %V 161 %X We propose two sum-product laws for imprecise Markov chains, and use these laws to derive two algorithms to efficiently compute lower and upper expectations for imprecise Markov chains under complete independence and epistemic irrelevance. These algorithms work for inferences that have a corresponding sum-product decomposition, and we argue that many well-known inferences fit their scope. We illustrate our results on a simple epidemiological example.
APA
De Bock, J., Erreygers, A. & Krak, T.. (2021). Sum-product laws and efficient algorithms for imprecise Markov chains. Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 161:1476-1485 Available from https://proceedings.mlr.press/v161/de-bock21a.html.

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