Publications: Journal Article: Complex Probabilistic Modeling with Recursive Relational Bayesian Networks

Journal Article
@Article
Artikel in Fachzeitschrift

Show entries of:

this year (2024) | last year (2023) | two years ago (2022) | Notes URL

Action:

Options:

Goto entry point

Author, Editor(s)

Author(s):

Jaeger, Manfred

dblp

BibTeX cite key*:

JaegerAMAI01

Title

Title*:

Complex Probabilistic Modeling with Recursive Relational Bayesian Networks

Journal

Journal Title*:	Annals of Mathematics and Artificial Intelligence	Journal's URL:	http://www.kluweronline.com/issn/1012-2443
Download URL for the article:		Language:	English

Publisher

Publisher's Name:	Kluwer	Publisher's URL:	http://www.wkap.nl/kaphtml.htm/HOMEPAGE
Publisher's Address:	Dordrecht, The Netherlands	ISSN:	1012-2443

Vol, No, pp, Date

Volume*:	32	Number:
Publishing Date:	2001
Pages*:	179-220	Number of VG Pages:	41
Page Start:		Page End:
Sequence Number:		DOI:

Note:
(LaTeX) Abstract:	A number of representation systems have been proposed that extend the purely propositional Bayesian network paradigm with representation tools for some types of first-order probabilistic dependencies. Examples of such systems are dynamic Bayesian networks and systems for knowledge based model construction. We can identify the representation of probabilistic relational models as a common well-defined semantic core of such systems. Recursive relational Bayesian networks (RRBNs) are a framework for the representation of probabilistic relational models. A main design goal for RRBNs is to achieve greatest possible expressiveness with as few elementary syntactic constructs as possible. The advantage of such an approach is that a system based on a small number of elementary constructs will be much more amenable to a thorough mathematical investigation of its semantic and algorithmic properties than a system based on a larger number of high-level constructs. In this paper we show that with RRBNs we have achieved our goal, by showing, first, how to solve within that framework a number of non-trivial representation problems. In the second part of the paper we show how to construct from a RRBN and a specific query, a standard Bayesian network in which the answer to the query can be computed with standard inference algorithms. Here the simplicity of the underlying representation framework greatly facilitates the development of simple algorithms and correctness proofs. As a result we obtain a construction algorithm that even for RRBNs that represent models for complex first-order and statistical dependencies generates standard Bayesian networks of size polynomial in the size of the domain given in a specific application instance.
URL for the Abstract:	http://www.mpi-sb.mpg.de/~jaeger/publications/AMAI01abstract.html
Categories, Keywords:
HyperLinks / References / URLs:
Copyright Message:
Personal Comments:
Download Access Level:	MPG

Correlation

MPG Unit:	Max-Planck-Institut für Informatik

MPG Subunit:	Programming Logics Group
Audience:	experts only
Appearance:	MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort

BibTeX Entry:

@ARTICLE{JaegerAMAI01,
AUTHOR = {Jaeger, Manfred},
TITLE = {Complex Probabilistic Modeling with Recursive Relational Bayesian Networks},
JOURNAL = {Annals of Mathematics and Artificial Intelligence},
PUBLISHER = {Kluwer},
YEAR = {2001},
VOLUME = {32},
PAGES = {179--220},
ADDRESS = {Dordrecht, The Netherlands},
ISBN = {1012-2443},
}

Entry last modified by Manfred Jaeger, 03/12/2010

Edit History (please click the blue arrow to see the details)

	Editor(s) Manfred Jaeger	Created 08/15/2001 03:38:52 PM
Revisions 12. 11. 10. 9. 8.	Editor(s) Manfred Jaeger Uwe Brahm Manfred Jaeger Manfred Jaeger Manfred Jaeger	Edit Dates 05/22/2003 07:55:46 PM 03/28/2002 12:59:45 AM 15/01/2002 15:49:25 15/01/2002 15:48:21 15/01/2002 15:47:54