Approximate factorisation of probability trees

Authors:
Irene Martínez;Serafín Moral;Carmelo Rodríguez;Antonio Salmerón
Affiliations:
Dept. Languages and Computation, University of Almería, Spain;Dept. Computer Science and Artificial Intelligence, University of Granada, Spain;Dept. Statistics and Applied Mathematics, University of Almería, Spain;Dept. Statistics and Applied Mathematics, University of Almería, Spain
Venue:
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2005

Citing 5
Cited 2

The computational complexity of probabilistic inference using Bayesian belief networks (research note)

Artificial Intelligence
Approximating probabilistic inference in Bayesian belief networks is NP-hard

Artificial Intelligence
LAZY propagation: a junction tree inference algorithm based on lazy evaluation

Artificial Intelligence
Importance sampling in Bayesian networks using probability trees

Computational Statistics & Data Analysis
Context-specific independence in Bayesian networks

UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence

Recursive probability trees for Bayesian networks

CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Learning recursive probability trees from probabilistic potentials

International Journal of Approximate Reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

Bayesian networks are efficient tools for probabilistic reasoning over large sets of variables, due to the fact that the joint distribution factorises according to the structure of the network, which captures conditional independence relations among the variables. Beyond conditional independence, the concept of asymmetric (or context specific) independence makes possible the definition of even more efficient reasoning schemes, based on the representation of probability functions through probability trees. In this paper we investigate how it is possible to achieve a finer factorisation by decomposing the original factors for which some conditions hold. We also introduce the concept of approximate factorisation and apply this methodology to the Lazy-Penniless propagation algorithm.