Related papers: Inference of collective Gaussian hidden Markov models

Inference of collective Gaussian hidden Markov models

URL: http://arxiv.org/abs/2107.11662v1
Date: Sat, 24 Jul 2021 17:49:01 GMT
Title: Inference of collective Gaussian hidden Markov models
Authors: Rahul Singh, Yongxin Chen
Abstract summary: We consider inference problems for a class of continuous state collective hidden Markov models. We propose an aggregate inference algorithm called collective Gaussian forward-backward algorithm.
Score: 8.348171150908724
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: We consider inference problems for a class of continuous state collective hidden Markov models, where the data is recorded in aggregate (collective) form generated by a large population of individuals following the same dynamics. We propose an aggregate inference algorithm called collective Gaussian forward-backward algorithm, extending recently proposed Sinkhorn belief propagation algorithm to models characterized by Gaussian densities. Our algorithm enjoys convergence guarantee. In addition, it reduces to the standard Kalman filter when the observations are generated by a single individual. The efficacy of the proposed algorithm is demonstrated through multiple experiments.

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