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Bayesian Theory and Applications$
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Paul Damien, Petros Dellaportas, Nicholas G. Polson, and David A. Stephens

Print publication date: 2013

Print ISBN-13: 9780199695607

Published to Oxford Scholarship Online: May 2013

DOI: 10.1093/acprof:oso/9780199695607.001.0001

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Bayesian dynamic modelling

Bayesian dynamic modelling

Chapter:
(p.145) 8 Bayesian dynamic modelling
Source:
Bayesian Theory and Applications
Author(s):

West Mike

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199695607.003.0008

This chapter focuses on some key models and ideas in Bayesian time series and forecasting, along with extracts from a few time series analysis and forecasting examples. It discusses specific modelling innovations that relate directly to the goals of addressing analysis of increasingly high-dimensional time series and nonlinear models. These include dynamic graphical and matrix models, dynamic matrix models for stochastic volatility, time-varying sparsity modelling, and nonlinear dynamical systems.

Keywords:   Bayesian time series analysis, forecasting, dynamic linear modelling

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