Online variational filtering and parameter learning
We present a variational method for online state estimation and parameter learning in state-space models (SSMs), a ubiquitous class of latent variable models for sequential data. As per standard batch variational techniques, we use stochastic gradients to simultaneously optimize a lower bound on the...
| Huvudupphovsmän: | , , , |
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| Materialtyp: | Conference item |
| Språk: | English |
| Publicerad: |
Curran Associates
2022
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