Probabilistic Inference and Bayesian Theorem on Rough Sets

Authors:
Yukari Yamauchi;Masao Mukaidono
Affiliations:
-;-
Venue:
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Year:
2000

Citing 6
Cited 0

Probabilistic logic

Artificial Intelligence
Comparison of rough-set and interval-set models for uncertain reasoning

Fundamenta Informaticae - Special issue: rough sets
Interval Approaches for Uncertain Reasoning

ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
Decision Rules, Bayes' Rule and Ruogh Sets

RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
A Generalized Decision Logic in Interval-Set-Valued Information Tables

RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Probabilistic Inference and Baysian Theorem Based on Logical Implication

RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The concept of (crisp) set is now extended to fuzzy set and rough set. The key notion of rough set is the two boundaries, the lower and upper approximations, and the lower approximation must be inside of the upper approximation. This inclusive condition makes the inference using rough sets complex: each approximation can not be determined independently. In this paper, the probabilistic inferences on rough sets based on two types of interpretation of If-Then rules, conditional probability and logical implication, are discussed. There are some interesting correlation between the lower and upper approximation after probabilistic inference.