Generalized Bayesian Filtering via Sequential Monte Carlo
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... (read more)
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Methods used in the Paper
| METHOD | TYPE | |
|---|---|---|
Gaussian Process
|
Non-Parametric Classification |