𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Inference Algorithms in Bayesian Networks and the Probanet System

✍ Scribed by Heping Pan; Daniel McMichael; Marta Lendjel

Publisher
Elsevier Science
Year
1998
Tongue
English
Weight
273 KB
Volume
8
Category
Article
ISSN
1051-2004

No coin nor oath required. For personal study only.

✦ Synopsis

This paper reviews and formalizes algorithms for probabilistic inferences upon causal probabilistic networks (CPN), also known as Bayesian networks, and introduces Probanet-a development environment for CPNs. Information fusion in CPNs is realized through updating joint probabilities of the variables upon the arrival of new evidences or new hypotheses. Kernel algorithms for some dominant methods of inferences are formalized from discontiguous, mathematics-oriented literatures, with gaps filled in with regards to computability and completeness. Probanet has been designed and developed as a generic shell, a development environment for CPN construction and application. The design aspects and current status of Probanet are described. 1998 Academic Press

πŸ“œ SIMILAR VOLUMES

Learning Bayesian networks in the space
Learning Bayesian networks in the space of structures by estimation of distribution algorithms
✍ Rosa Blanco; IΓ±aki Inza; Pedro LarraΓ±aga πŸ“‚ Article πŸ“… 2003 πŸ› John Wiley and Sons 🌐 English βš– 155 KB

The induction of the optimal Bayesian network structure is NP-hard, justifying the use of search heuristics. Two novel population-based stochastic search approaches, univariate marginal distribution algorithm (UMDA) and population-based incremental learning (PBIL), are used to learn a Bayesian netwo