Journal article
Bias of particle approximations to optimal filter derivative
- Abstract:
-
In many applications, a state-space model depends on a parameter which needs to be inferred from data in an online manner. In the maximum likelihood approach, this can be achieved using stochastic gradient search, where the underlying gradient estimation is based on the optimal filter and the optimal filter derivative. However, the optimal filter and its derivative are not analytically tractable for a non-linear state-space model and need to be approximated numerically. In [22], a particle ap...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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Bibliographic Details
- Publisher:
- Society for Industrial and Applied Mathematics Publisher's website
- Journal:
- SIAM Journal on Control and Optimization Journal website
- Volume:
- 59
- Issue:
- 1
- Pages:
- 727–748
- Publication date:
- 2021-02-25
- Acceptance date:
- 2020-11-30
- DOI:
- EISSN:
-
1095-7138
- ISSN:
-
0363-0129
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1147971
- Local pid:
- pubs:1147971
- Deposit date:
- 2020-12-07
Terms of use
- Copyright holder:
- Society for Industrial and Applied Mathematics.
- Copyright date:
- 2021
- Rights statement:
- © 2021, Society for Industrial and Applied Mathematics.
- Notes:
- This is the accepted manuscript version of the article. The final version is available from Society for Industrial and Applied Mathematics at: https://doi.org/10.1137/18M1217024
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