๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Graphical dynamic linear models: specification, use and graphical transformations

โœ Scribed by Beatriz Lacruz; Pilar Lasala; Alberto Lekuona

Publisher
Elsevier Science
Year
2000
Tongue
English
Weight
199 KB
Volume
24
Category
Article
ISSN
0888-613X

No coin nor oath required. For personal study only.

โœฆ Synopsis

In this work, we propose a dynamic graphical model as a tool for Bayesian inference and forecasting in dynamic systems described by a series which is dependent on a state vector evolving according to a Markovian law. We build sequential algorithms for the probabilities propagation. This sequentiality turns out to be represented by the dynamic graphical structure after carrying out several goal-oriented sequential graphical transformations.

๐Ÿ“œ SIMILAR VOLUMES

Transformations and dynamic linear model
Transformations and dynamic linear models
โœ Peter J. Lenk; Chih-Ling Tsai ๐Ÿ“‚ Article ๐Ÿ“… 1990 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 816 KB

The dynamic linear model (DLM) with additive Gaussian errors provides a useful statistical tool that is easily implemented because of the simplicity of updating a normal model that has a natural conjugate prior. If the model is not linear or if it does not have additive Gaussian errors, then numeric

Dynamic graphical models and nonhomogene
Dynamic graphical models and nonhomogeneous hidden Markov models
โœ Beatriz Lacruz; Pilar Lasala; Alberto Lekuona ๐Ÿ“‚ Article ๐Ÿ“… 2000 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 153 KB

We propose a dynamic graphical model which generalizes nonhomogeneous hidden Markov models. Inference and forecast procedures are developed. A comparison with an exact propagation algorithm is established and equivalence is stated.