Sum-Product Laws and Efficient Algorithms for Imprecise Markov Chains
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
Text
De Bock et al. "Sum-Product Laws and Efficient Algorithms for Imprecise Markov Chains." Uncertainty in Artificial Intelligence, 2021.Markdown
[De Bock et al. "Sum-Product Laws and Efficient Algorithms for Imprecise Markov Chains." Uncertainty in Artificial Intelligence, 2021.](https://mlanthology.org/uai/2021/debock2021uai-sumproduct/)BibTeX
@inproceedings{debock2021uai-sumproduct,
title = {{Sum-Product Laws and Efficient Algorithms for Imprecise Markov Chains}},
author = {De Bock, Jasper and Erreygers, Alexander and Krak, Thomas},
booktitle = {Uncertainty in Artificial Intelligence},
year = {2021},
pages = {1476-1485},
volume = {161},
url = {https://mlanthology.org/uai/2021/debock2021uai-sumproduct/}
}