Generalized Bayesian Filtering via Sequential Monte Carlo

• URL: http://arxiv.org/abs/2002.09998v2
• Date: Wed, 21 Oct 2020 15:05:58 GMT
• Title: Generalized Bayesian Filtering via Sequential Monte Carlo
• Authors: Ayman Boustati, \"Omer Deniz Akyildiz, Theodoros Damoulas, Adam M. Johansen
• Abstract summary: We introduce a framework for inference in general state-space hidden Markov models (HMMs) under likelihood misspecification. We leverage the loss-theoretic perspective of Generalized Bayesian Inference (GBI) to define generalised filtering recursions in HMMs. We observe improved performance over both standard filtering algorithms and other robust filters.
• Score: 12.789129084258409
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a framework for inference in general state-space hidden Markov models (HMMs) under likelihood misspecification. In particular, we leverage the loss-theoretic perspective of Generalized Bayesian Inference (GBI) to define generalised filtering recursions in HMMs, that can tackle the problem of inference under model misspecification. In doing so, we arrive at principled procedures for robust inference against observation contamination by utilising the $\beta$-divergence. Operationalising the proposed framework is made possible via sequential Monte Carlo methods (SMC), where most standard particle methods, and their associated convergence results, are readily adapted to the new setting. We apply our approach to object tracking and Gaussian process regression problems, and observe improved performance over both standard filtering algorithms and other robust filters.

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