Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
A tutorial on learning with Bayesian networks
Learning in graphical models
Probabilistic Expert Systems
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Uncertain Information Processing in Expert Systems
Uncertain Information Processing in Expert Systems
d-Separation: From Theorems to Algorithms
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A new method for MR grayscale inhomogeneity correction
Artificial Intelligence Review
New spatial based MRI image de-noising algorithm
Artificial Intelligence Review
Hi-index | 0.00 |
Consider a family $${(X_i)_{i \in I}}$$ of random variables endowed with the structure of a Bayesian network, and a subset S of I. This paper examines the problem of computing the probability distribution of the subfamily $${(X_{a})_{a \in S}}$$ (respectively the probability distribution of $${ (X_{b})_{b \in {\bar{S}}}}$$ , where $${{\bar{S}} = I - S}$$ , conditional on $${(X_{a})_{a \in S}}$$ ). This paper presents some theoretical results that makes it possible to compute joint and conditional probabilities over a subset of variables by computing over separate components. In other words, it is demonstrated that it is possible to decompose this task into several parallel computations, each related to a subset of S (respectively of $${{\bar{S}}}$$ ); these partial results are then put together as a final product. In computing the probability distribution over $${(X_a)_{a \in S}}$$ , this procedure results in the production of a structure of level two Bayesian network structure for S.