
Open access
Author
Date
2021Type
- Journal Article
ETH Bibliography
yes
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Abstract
We consider the filtering of continuous-time finite-state hidden Markov models, where the rate and observation matrices depend on unknown time-dependent parameters, for which no prior or stochastic model is available. We quantify and analyze how the induced uncertainty may be propagated through time as we collect new observations, and used to simultaneously provide robust estimates of the hidden signal and to learn the unknown parameters, via techniques based on pathwise filtering and new results on the optimal control of rough differential equations. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000494884Publication status
publishedExternal links
Journal / series
Electronic Journal of ProbabilityVolume
Pages / Article No.
Publisher
Institute of Mathematical StatisticsSubject
hidden Markov model; filtering; parameter uncertainty; rough paths; pathwise optimal controlOrganisational unit
03658 - Schweizer, Martin / Schweizer, Martin
Funding
184647 - Mean-variance problems in mathematical finance: Beyond the classical theory (SNF)
Related publications and datasets
Is new version of: http://hdl.handle.net/20.500.11850/464049
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ETH Bibliography
yes
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