A probabilistic inference rule is a general rule that provides bounds on a target probability given constraints on a number of input probabilities. Example: from P AjB T r infer P XAjB P 1 ร r; 1. Rules of this kind have been studied extensively as a deduction method for propositional probabilistic
An Evaluation of Explanations of Probabilistic Inference
โ Scribed by Henri J. Suermondt; Gregory F. Cooper
- Publisher
- Elsevier Science
- Year
- 1993
- Tongue
- English
- Weight
- 455 KB
- Volume
- 26
- Category
- Article
- ISSN
- 0010-4809
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
Nilsson's Probabilistic Logic is a set-theoretic mechanism for reasoning with uncertainty. We propose a new way of looking at the probability constraints enforced by the framework, which allows the expert to include conditional probabilities in the semantic tree, thus making Probabilistic Logic more
We identify various situations in probabilistic intelligent systems in which conditionals (rules) as mathematical entities as well as their conditional logic operations are needed. In discussing Bayesian updating procedure and belief function construction, we provide a new method for modeling if. .
We investigate the complexity of probabilistic inference from knowledge bases that encode probability distributions on finite domain relational structures. Our interest here lies in the complexity in terms of the domain under consideration in a specific application instance. We obtain the result tha
Partial abductive inference in Bayesian belief networks (BBNs) is intended as the process of generating the K most probable conยฎgurations for a set of unobserved variables (the explanation set). This problem is NP-hard and so exact computation is not always possible. In previous works genetic algori