Multistate Analysis with Infinite Mixtures of Markov Chains
Abstract
Driven by applications in clinical medicine and business, we address the problem of modeling trajectories over multiple states. We build on well-known methods from survival analysis and introduce a family of sequence models based on localized Bayesian Markov chains. We develop inference and prediction algorithms, and we apply the model to real-world data, demonstrating favorable empirical results. Our approach provides a practical and effective alternative to plain Markov chains and to existing (finite) mixture models; It retains the simplicity and computational benefits of the former while matching or exceeding the predictive performance of the latter.
Cite
Text
Maystre et al. "Multistate Analysis with Infinite Mixtures of Markov Chains." Uncertainty in Artificial Intelligence, 2022.Markdown
[Maystre et al. "Multistate Analysis with Infinite Mixtures of Markov Chains." Uncertainty in Artificial Intelligence, 2022.](https://mlanthology.org/uai/2022/maystre2022uai-multistate/)BibTeX
@inproceedings{maystre2022uai-multistate,
title = {{Multistate Analysis with Infinite Mixtures of Markov Chains}},
author = {Maystre, Lucas and Wu, Tiffany and Sanchis-Ojeda, Roberto and Jebara, Tony},
booktitle = {Uncertainty in Artificial Intelligence},
year = {2022},
pages = {1350-1359},
volume = {180},
url = {https://mlanthology.org/uai/2022/maystre2022uai-multistate/}
}