IDS: A Divide-and-Conquer Algorithm for Inference in Polytree-Shaped Credal Networks

J. C. F. da Rocha, C. P. de Campos, F. G. Cozman

Research output: Chapter in Book/Report/Conference proceedingConference contribution


A credal network is a graph-theoretic model that represents imprecision in joint probability distributions. An inference in a credal net aims at computing an interval for the probability of an event of interest. Algorithms for inference in credal networks can be divided into exact and approximate. The selection of an algorithm is based on a trade off that ponders how much time someone wants to spend in a particular calculation against the quality of the computed values. This paper presents an algorithm, called IDS, that combines exact and approximate methods for computing inferences in polytree-shaped credal networks. The algorithm provides an approach to trade time and precision when making inferences in credal nets
Original languageEnglish
Title of host publicationAnais do Encontro Nacional de Inteligencia Artificial
Number of pages10
Publication statusPublished - 2005

Bibliographical note

(oral presentation, blind peer reviewed by >3 reviewers)


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