Finding, Scoring and Explaining Arguments in Bayesian Networks

• URL: http://arxiv.org/abs/2112.00799v1
• Date: Tue, 30 Nov 2021 12:41:04 GMT
• Title: Finding, Scoring and Explaining Arguments in Bayesian Networks
• Authors: Jaime Sevilla
• Abstract summary: We define a notion of independent arguments, and propose an algorithm to extract a list of relevant, independent arguments. To demonstrate the relevance of the arguments, we show how we can use the extracted arguments to approximate message passing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a new approach to explain Bayesian Networks. The approach revolves around a new definition of a probabilistic argument and the evidence it provides. We define a notion of independent arguments, and propose an algorithm to extract a list of relevant, independent arguments given a Bayesian Network, a target node and a set of observations. To demonstrate the relevance of the arguments, we show how we can use the extracted arguments to approximate message passing. Finally, we show a simple scheme to explain the arguments in natural language.

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