d-Separation: From Theorems to Algorithms

📝 Original Info

• Title: d-Separation: From Theorems to Algorithms
• ArXiv ID: 1304.1505
• Date: 2013-04-08
• Authors: Researchers from original ArXiv paper

📝 Abstract

An efficient algorithm is developed that identifies all independencies implied by the topology of a Bayesian network. Its correctness and maximality stems from the soundness and completeness of d-separation with respect to probability theory. The algorithm runs in time O (l E l) where E is the number of edges in the network.

💡 Deep Analysis

Deep Dive into d-Separation: From Theorems to Algorithms.

An efficient algorithm is developed that identifies all independencies implied by the topology of a Bayesian network. Its correctness and maximality stems from the soundness and completeness of d-separation with respect to probability theory. The algorithm runs in time O (l E l) where E is the number of edges in the network.

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