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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2023-01, Vol.45 (1), p.1150-1161
Hauptverfasser:	Zhao, Yuan, Nassar, Josue, Jordan, Ian, Bugallo, Monica, Park, Il Memming
Format:	Artikel
Sprache:	eng
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!