Conditioning Methods for Exact and Approximate Inference in Causal Networks

📝 Original Info

• Title: Conditioning Methods for Exact and Approximate Inference in Causal Networks
• ArXiv ID: 1302.4939
• Date: 2013-02-21
• Authors: Researchers from original ArXiv paper

📝 Abstract

We present two algorithms for exact and approximate inference in causal networks. The first algorithm, dynamic conditioning, is a refinement of cutset conditioning that has linear complexity on some networks for which cutset conditioning is exponential. The second algorithm, B-conditioning, is an algorithm for approximate inference that allows one to trade-off the quality of approximations with the computation time. We also present some experimental results illustrating the properties of the proposed algorithms.

💡 Deep Analysis

Deep Dive into Conditioning Methods for Exact and Approximate Inference in Causal Networks.

We present two algorithms for exact and approximate inference in causal networks. The first algorithm, dynamic conditioning, is a refinement of cutset conditioning that has linear complexity on some networks for which cutset conditioning is exponential. The second algorithm, B-conditioning, is an algorithm for approximate inference that allows one to trade-off the quality of approximations with the computation time. We also present some experimental results illustrating the properties of the proposed algorithms.

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