'Conditional Inter-Causally Independent' Node Distributions, a Property of 'Noisy-Or' Models

📝 Original Info

• Title: ‘Conditional Inter-Causally Independent’ Node Distributions, a Property of ‘Noisy-Or’ Models
• ArXiv ID: 1303.5704
• Date: 2013-03-26
• Authors: Researchers from original ArXiv paper

📝 Abstract

This paper examines the interdependence generated between two parent nodes with a common instantiated child node, such as two hypotheses sharing common evidence. The relation so generated has been termed "intercausal." It is shown by construction that inter-causal independence is possible for binary distributions at one state of evidence. For such "CICI" distributions, the two measures of inter-causal effect, "multiplicative synergy" and "additive synergy" are equal. The well known "noisy-or" model is an example of such a distribution. This introduces novel semantics for the noisy-or, as a model of the degree of conflict among competing hypotheses of a common observation.

💡 Deep Analysis

Deep Dive into "Conditional Inter-Causally Independent" Node Distributions, a Property of "Noisy-Or" Models.

This paper examines the interdependence generated between two parent nodes with a common instantiated child node, such as two hypotheses sharing common evidence. The relation so generated has been termed “intercausal.” It is shown by construction that inter-causal independence is possible for binary distributions at one state of evidence. For such “CICI” distributions, the two measures of inter-causal effect, “multiplicative synergy” and “additive synergy” are equal. The well known “noisy-or” model is an example of such a distribution. This introduces novel semantics for the noisy-or, as a model of the degree of conflict among competing hypotheses of a common observation.

📄 Full Content

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