Multistate analysis with infinite mixtures of Markov chains

Lucas Maystre, Tiffany Wu, Roberto Sanchis-Ojeda, Tony Jebara
Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, PMLR 180:1350-1359, 2022.

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 this Paper

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.} }
Endnote
%0 Conference Paper %T Multistate analysis with infinite mixtures of Markov chains %A Lucas Maystre %A Tiffany Wu %A Roberto Sanchis-Ojeda %A Tony Jebara %B Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2022 %E James Cussens %E Kun Zhang %F pmlr-v180-maystre22a %I PMLR %P 1350--1359 %U https://proceedings.mlr.press/v180/maystre22a.html %V 180 %X 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.
APA
Maystre, L., Wu, T., Sanchis-Ojeda, R. & Jebara, T.. (2022). Multistate analysis with infinite mixtures of Markov chains. Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 180:1350-1359 Available from https://proceedings.mlr.press/v180/maystre22a.html.

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