Multistate analysis with infinite mixtures of Markov chains


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.


November 2022 | NeurIPS

Society of Agents: Regrets Bounds of Concurrent Thompson Sampling

Yan Chen, Perry Dong, Qinxun Bai, Maria Dimakopoulou, Wei Xu, Zhengyuan Zhou

November 2022 | NeurIPS

Temporally-Consistent Survival Analysis

Lucas Maystre, Daniel Russo

November 2022 | NeurIPS

Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders

Olivier Jeunen, Ciarán M. Gilligan-Lee, Rishabh Mehrotra, Mounia Lalmas