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Generalised Bayesian Filtering via Sequential Monte Carlo
Ayman Boustati · Omer Deniz Akyildiz · Theodoros Damoulas · Adam Johansen

Thu Dec 10 09:00 AM -- 11:00 AM (PST) @ Poster Session 5 #1599
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 the standard particle methods, and their associated convergence results, are readily adapted to the new setting. We demonstrate our approach to object tracking and Gaussian process regression problems, and observe improved performance over standard filtering algorithms.

Author Information

Ayman Boustati (University of Warwick)
Omer Deniz Akyildiz (University of Warwick and The Alan Turing Institute)
Theodoros Damoulas (University of Warwick & The Alan Turing Institute)
Adam Johansen (University of Warwick)

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