Computing Probability Intervals Under Independency Constraints

📝 Original Info

• Title: Computing Probability Intervals Under Independency Constraints
• ArXiv ID: 1304.1140
• Date: 2013-04-05
• Authors: Researchers from original ArXiv paper

📝 Abstract

Many AI researchers argue that probability theory is only capable of dealing with uncertainty in situations where a full specification of a joint probability distribution is available, and conclude that it is not suitable for application in knowledge-based systems. Probability intervals, however, constitute a means for expressing incompleteness of information. We present a method for computing such probability intervals for probabilities of interest from a partial specification of a joint probability distribution. Our method improves on earlier approaches by allowing for independency relationships between statistical variables to be exploited.

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Deep Dive into Computing Probability Intervals Under Independency Constraints.

Many AI researchers argue that probability theory is only capable of dealing with uncertainty in situations where a full specification of a joint probability distribution is available, and conclude that it is not suitable for application in knowledge-based systems. Probability intervals, however, constitute a means for expressing incompleteness of information. We present a method for computing such probability intervals for probabilities of interest from a partial specification of a joint probability distribution. Our method improves on earlier approaches by allowing for independency relationships between statistical variables to be exploited.

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