Updating Probabilities in Multiply-Connected Belief Networks

📝 Original Info

• Title: Updating Probabilities in Multiply-Connected Belief Networks
• ArXiv ID: 1304.2377
• Date: 2013-04-10
• Authors: Researchers from original ArXiv paper

📝 Abstract

This paper focuses on probability updates in multiply-connected belief networks. Pearl has designed the method of conditioning, which enables us to apply his algorithm for belief updates in singly-connected networks to multiply-connected belief networks by selecting a loop-cutset for the network and instantiating these loop-cutset nodes. We discuss conditions that need to be satisfied by the selected nodes. We present a heuristic algorithm for finding a loop-cutset that satisfies these conditions.

💡 Deep Analysis

Deep Dive into Updating Probabilities in Multiply-Connected Belief Networks.

This paper focuses on probability updates in multiply-connected belief networks. Pearl has designed the method of conditioning, which enables us to apply his algorithm for belief updates in singly-connected networks to multiply-connected belief networks by selecting a loop-cutset for the network and instantiating these loop-cutset nodes. We discuss conditions that need to be satisfied by the selected nodes. We present a heuristic algorithm for finding a loop-cutset that satisfies these conditions.

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