creators_name: Bellot, D
creators_name: Bessiere, P
type: confpaper
datestamp: 2004-08-10
lastmod: 2011-03-11 08:55:39
metadata_visibility: show
title: Approximate Discrete Probability Distribution Representation using a Multi-ResolutionBinary Tree
ispublished: pub
subjects: comp-sci-robot
full_text_status: public
abstract: Computing and storing probabilities is a hard problem as soon as one has to deal with complex distributions over multiples random variables. The problem of efficient representation of probability distributions is central in term of computational efficiency in the field of probabilistic reasoning. The main problem arises when dealing with joint probability distributions over a set of random variables: they are always represented using huge probability arrays. In this paper, a new method based on a binary-tree representation
is introduced in order to store efficiently very large joint distributions. Our approach approximates any multidimensional joint distributions using an adaptive discretization of the space. We make the assumption that the lower is the probability mass of a particular region of feature space, the larger is the discretization step. This assumption leads to a very optimized representation in term of time and memory. The other advantages of our approach are the ability to refine dynamically the distribution every time it is needed leading to a more accurate representation of the probability
distribution and to an anytime representation of the distribution.
date: 2003
date_type: published
refereed: FALSE
citation: Bellot, Dr D and Bessiere, Dr P (2003) Approximate Discrete Probability Distribution Representation using a Multi-ResolutionBinary Tree. [Conference Paper]
document_url: http://cogprints.org/3752/1/Bellot03.pdf