Approximate smoothing and parameter estimation in high-dimensional state-space models

We present approximate algorithms for performing smoothing in a class of high-dimensional state-space models via sequential Monte Carlo methods (particle filters). In high dimensions, a prohibitively large number of Monte Carlo samples (particles), growing exponentially in the dimension of the state...

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Bibliographic Details
Main Authors: Axel Finke, Sumeetpal S. Singh
Format: Default Article
Published: 2017
Subjects:
high dimensions
smoothing
particle filter
sequential Monte Carlo
state-space model
Online Access:https://hdl.handle.net/2134/12845813.v1
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