They are based on the use of probability trees to represent probability potentials, using the Kullback-Leibler cross entropy as a measure of the error of the approximation.Different alternatives are presente ..." This paper presents non-random algorithms for approximate computation in Bayesian networks.For example, it is unclear why a hypertree organization of agents was imposed.This study focuses on the necessity of MSBNs for representation of uncertain knowledge in CMADISs.They are based on the use of probability trees to represent probability potentials, using the Kullback-Leibler cross entropy as a measure of the error of the approximation. Influence diagrams are a powerful graphic representation for decision models, complementary to decision trees.Different alternatives are presented and tested in several experiments with difficult propagation problems. Influence diagrams and decision trees are different graphic representations for the same underlying mathematical model and operations.We identify a small set of fundamental choices which logically implies a MSBN or some equivalent representations.
Modelling sequential data is important in many areas of science and engineering.
Multiply sectioned Bayesian networks (MSBNs) provide one framework for agents to estimate the state of a domain.
Existing methods for multi-agent inference in MSBNs are based on linked junction forests (LJFs).
The main point we intend to study in this work is JT-based inference in Bayesian networks.
Apart from undertaking the triangulation problem itself, we have achieved a great improvement for the compilation in BNs.