Abstract
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 language | English |
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| Title of host publication | Anais do Encontro Nacional de Inteligencia Artificial |
| Pages | 553-562 |
| Number of pages | 10 |
| Publication status | Published - 2005 |