Uncertainty and filtering of hidden Markov models in discrete time

Uncertainty and filtering of hidden Markov models in discrete time

We consider the problem of filtering an unseen Markov chain from noisy observations, in the presence of uncertainty regarding the parameters of the processes involved. Using the theory of nonlinear expectations, we describe the uncertainty in terms of a penalty function, which can be propagated forw...

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Bibliografiske detaljer
Hovedforfatter: Cohen, SN
Format: Journal article
Sprog:English
Udgivet: SpringerOpen 2020
Beskrivelse
Summary:We consider the problem of filtering an unseen Markov chain from noisy observations, in the presence of uncertainty regarding the parameters of the processes involved. Using the theory of nonlinear expectations, we describe the uncertainty in terms of a penalty function, which can be propagated forward in time in the place of the filter. We also investigate a simple control problem in this context.

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