Streaming Variational Monte Carlo

Nonlinear state-space models are powerful tools to describe dynamical structures in complex time series. In a streaming setting where data are processed one sample at a time, simultaneous inference of the state and its nonlinear dynamics has posed significant challenges in practice. We develop a nov...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2023-01, Vol.45 (1), p.1150-1161
Main Authors: Zhao, Yuan, Nassar, Josue, Jordan, Ian, Bugallo, Monica, Park, Il Memming
Format: Article
Language:English
Subjects:
Bayes methods
Bayesian machine learning
Complexity
Filtration
Gaussian process
Inference
Monte Carlo methods
Nonlinear dynamics
Nonlinear state-space modeling
online filtering
Optimization
Proposals
Smoothing methods
State space models
State-space methods
Time series analysis
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