Equivalent Characterization of a Class of Conditional Probabilistic Independencies

Authors:
S. K. Michael Wong;Cory J. Butz
Affiliations:
-;-
Venue:
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Year:
1998

Citing 9
Cited 1

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
The computational complexity of probabilistic inference using Bayesian belief networks (research note)

Artificial Intelligence
Approximating probabilistic inference in Bayesian belief networks is NP-hard

Artificial Intelligence
An Extended Relational Data Model For Probabilistic Reasoning

Journal of Intelligent Information Systems
Multivalued dependencies and a new normal form for relational databases

ACM Transactions on Database Systems (TODS)
On the Desirability of Acyclic Database Schemes

Journal of the ACM (JACM)
Uncertain Information Processing in Expert Systems

Uncertain Information Processing in Expert Systems
A method for implementing a probabilistic model as a relational database

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Testing implication of probabilistic dependencies

UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence

Evolutionary algorithm for PCB inspection

International Journal of Knowledge-based and Intelligent Engineering Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Markov networks utilize nonembedded probabilistic conditional independencies in order to provide an economical representation of a joint distribution in uncertainty management. In this paper we study several properties of nonembedded conditional independencies and show that they are in fact equivalent. The results presented here not only show the useful characteristics of an important subclass of probabilistic conditional independencies, but further demonstrate the relationship between relational theory and probabilistic reasoning.