Minimum Error Tree Decomposition

📝 Original Info

• Title: Minimum Error Tree Decomposition
• ArXiv ID: 1304.1103
• Date: 2013-04-05
• Authors: Researchers from original ArXiv paper

📝 Abstract

This paper describes a generalization of previous methods for constructing tree-structured belief network with hidden variables. The major new feature of the described method is the ability to produce a tree decomposition even when there are errors in the correlation data among the input variables. This is an important extension of existing methods since the correlational coefficients usually cannot be measured with precision. The technique involves using a greedy search algorithm that locally minimizes an error function.

💡 Deep Analysis

Deep Dive into Minimum Error Tree Decomposition.

This paper describes a generalization of previous methods for constructing tree-structured belief network with hidden variables. The major new feature of the described method is the ability to produce a tree decomposition even when there are errors in the correlation data among the input variables. This is an important extension of existing methods since the correlational coefficients usually cannot be measured with precision. The technique involves using a greedy search algorithm that locally minimizes an error function.

📄 Full Content

Reference