zbMATH — the first resource for mathematics

Geometry Search for the term Geometry in any field. Queries are case-independent.
Funct* Wildcard queries are specified by * (e.g. functions, functorial, etc.). Otherwise the search is exact.
"Topological group" Phrases (multi-words) should be set in "straight quotation marks".
au: Bourbaki & ti: Algebra Search for author and title. The and-operator & is default and can be omitted.
Chebyshev | Tschebyscheff The or-operator | allows to search for Chebyshev or Tschebyscheff.
"Quasi* map*" py: 1989 The resulting documents have publication year 1989.
so: Eur* J* Mat* Soc* cc: 14 Search for publications in a particular source with a Mathematics Subject Classification code (cc) in 14.
"Partial diff* eq*" ! elliptic The not-operator ! eliminates all results containing the word elliptic.
dt: b & au: Hilbert The document type is set to books; alternatively: j for journal articles, a for book articles.
py: 2000-2015 cc: (94A | 11T) Number ranges are accepted. Terms can be grouped within (parentheses).
la: chinese Find documents in a given language. ISO 639-1 language codes can also be used.

a & b logic and
a | b logic or
!ab logic not
abc* right wildcard
"ab c" phrase
(ab c) parentheses
any anywhere an internal document identifier
au author, editor ai internal author identifier
ti title la language
so source ab review, abstract
py publication year rv reviewer
cc MSC code ut uncontrolled term
dt document type (j: journal article; b: book; a: book article)
A factorized representation of independence of causal influence and lazy propagation. (English) Zbl 1113.68528
Summary: The efficiency of algorithms for probabilistic inference in Bayesian networks can be improved by exploiting independence of causal influence. The factorized representation of independence of causal influence offers a factorized decomposition of certain independence of causal influence models. We describe how LAZY propagation - a junction tree based inference algorithm - easily can be extended to take advantage of the decomposition offered by the factorized representation. We introduce two extensions to the factorized representation easing the knowledge acquisition task and reducing the space complexity of the representation exponentially in the state space size of the effect variable of an independence of causal influence model. We describe how the factorized representation can be used to solve tasks such as calculating the maximum a posteriori hypotheses, the maximum expected utility, and the most probable configuration. Finally, the results of an empirical evaluation indicate that considerable performance improvements can be obtained using LAZY propagation combined with the factorized representation compared to LAZY propagation performed in junction trees constructed after either parent divorcing or temporal transformation have been applied to the Bayesian network.

68T37Reasoning under uncertainty
68T05Learning and adaptive systems
Full Text: DOI