Key Concepts

Context

• In BayesiaLab, nearly all learning and analysis functions are based on principles and metrics from the field of Information Theory.
• In this section, we summarize some of these concepts and attempt to relate them to the corresponding BayesiaLab functions.
• Furthermore, we include several relevant statistical concepts for understanding BayesiaLab's estimates and visualizations.

Content

• Entropy
• Log-Loss
• Maximum-Likelihood Estimation
• Minimum Description Length Score (MDL Score)
• Mutual-Information
• Normalized Mutual Information
• Bayes Rule and Bayes Theorem
• Conditional Probability Table-
• Contingency Table Fit
• Deviance
• Entropy
• Hellinger-Distance
• Information Gain
• Joint Probability and Joint Probability Distribution
• Kullback-Leibler Divergence and Arc Force
• Latent Variables, Factors, and Hidden Nodes vs. Manifest Variables
• Markov Blanket
• Means and Values of Nodes
• Pearson Correlation
• Structural Coefficient
• Total Effect