Knowledge Integration for Conditional Probability Assessments

📝 Original Info

• Title: Knowledge Integration for Conditional Probability Assessments
• ArXiv ID: 1303.5404
• Date: 2013-03-25
• Authors: Researchers from original ArXiv paper

📝 Abstract

In the probabilistic approach to uncertainty management the input knowledge is usually represented by means of some probability distributions. In this paper we assume that the input knowledge is given by two discrete conditional probability distributions, represented by two stochastic matrices P and Q. The consistency of the knowledge base is analyzed. Coherence conditions and explicit formulas for the extension to marginal distributions are obtained in some special cases.

💡 Deep Analysis

Deep Dive into Knowledge Integration for Conditional Probability Assessments.

In the probabilistic approach to uncertainty management the input knowledge is usually represented by means of some probability distributions. In this paper we assume that the input knowledge is given by two discrete conditional probability distributions, represented by two stochastic matrices P and Q. The consistency of the knowledge base is analyzed. Coherence conditions and explicit formulas for the extension to marginal distributions are obtained in some special cases.

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