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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| Format: | Journal article |
| Language: | English |
| Published: |
SpringerOpen
2020
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