Learning and approximate inference in dynamic hierarchical models

DOI10.1016/j.csda.2007.01.001zbMath1452.62615OpenAlexW1966986704MaRDI QIDQ1020885

Tom Heskes, Bart Bakker

Publication date: 2 June 2009

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/2066/34584

zbMATH Keywords

maximum likelihood estimationtime seriesdynamic linear modelvariational approximationexpectation propagation

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15)

Related Items (2)

Editorial: 2nd special issue on statistical signal extraction and filtering ⋮ The explicit form of expectation propagation for a simple statistical model

Cites Work

• Bayesian forecasting and dynamic models.
• Factorial hidden Markov models
• Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology
• Semiparametric Stochastic Mixed Models for Longitudinal Data
• Bayesian Forecasting of Multinomial Time Series Through Conditional Gaussian Dynamic Models
• Tree-based reparameterization framework for analysis of sum-product and related algorithms
• 10.1162/153244304322765658
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Learning and approximate inference in dynamic hierarchical models