Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models: a 
Variational Approach

Chiappa, S; Barber, D

Local TagsRelease HistoryDetailsSummary

Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models: a Variational Approach

Chiappa, S., & Barber, D.(2007). Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models: a Variational Approach (161). Tübingen, Germany: Max Planck Institute for Biological Cybernetics.

Item is Released

show all hide all

Basic

show hide

Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CE85-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-87D9-1

Genre: Report

Files

show Files

hide Files

:

MPIK-TR-161.pdf (Publisher version), 532KB

View Save

File Permalink:
http://hdl.handle.net/21.11116/0000-0002-87A0-0

Name:
MPIK-TR-161.pdf

Description:
-

Visibility:
Public

MIME-Type / Checksum:
application/pdf / [MD5]

Technical Metadata:

View

Copyright Date:
-

Copyright Info:
-

License:
-

Locators

show

Creators

show

hide

Creators:
Chiappa, S^{1, 2}, Author
Barber, D, Author

Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794

Content

show

hide

Free keywords: -

Abstract: We describe two related models to cluster multidimensional time-series under the assumption of an underlying linear Gaussian dynamical process. In the first model, times-series are assigned to the same cluster when they show global similarity in their dynamics, while in the second model times-series are assigned to the same cluster when they show simultaneous similarity. Both models are based on Dirichlet Mixtures of Bayesian Linear Gaussian State-Space Models in order to (semi) automatically determine an appropriate number of components in the mixture, and to additionally bias the components to a parsimonious parameterization. The resulting models are formally intractable and to deal with this we describe a deterministic approximation based on a novel implementation of Variational Bayes.

Details

show

hide

Language(s):

Dates: Published in Print: 2007-03

Publication Status: Published in print

Pages: 32

Publishing info: Tübingen, Germany : Max Planck Institute for Biological Cybernetics

Table of Contents: -

Rev. Type: -

Identifiers: Report Nr.: 161
BibTex Citekey: 4917

Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show

hide

Title: Technical Report of the Max Planck Institute for Biological Cybernetics

Source Genre: Series

Creator(s):

Affiliations:

Publ. Info: -

Pages: - Volume / Issue: 161 Sequence Number: - Start / End Page: - Identifier: -