UAI 2022poster0 citations
Multistate analysis with infinite mixtures of Markov chains
Lucas Maystre, Tiffany Wu, Roberto Sanchis-Ojeda, Tony Jebara
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.
BibTeX
@InProceedings{pmlr-v180-maystre22a,
title = {Multistate analysis with infinite mixtures of {Markov} chains},
author = {Maystre, Lucas and Wu, Tiffany and Sanchis-Ojeda, Roberto and Jebara, Tony},
booktitle = {Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence},
pages = {1350--1359},
year = {2022},
editor = {Cussens, James and Zhang, Kun},
volume = {180},
series = {Proceedings of Machine Learning Research},
month = {01--05 Aug},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v180/maystre22a/maystre22a.pdf},
url = {https://proceedings.mlr.press/v180/maystre22a.html},
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.}
}