Bayesian Network Algorithms: Structure Learning, Parameter Learning, and Inference

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Resource Overview

Bayesian network algorithms with comprehensive capabilities for structure learning, parameter learning, and probabilistic inference, implemented through computational methods and statistical techniques.

Detailed Documentation

In this article, we explore the three primary functions of Bayesian network algorithms: structure learning, parameter learning, and inference. Bayesian networks are probabilistic graphical models that represent dependencies among variables and enable probability predictions for future events. Structure learning algorithms automatically derive network topology from data using techniques like constraint-based methods (PC algorithm) or score-based approaches (Hill-Climbing, BDe score). Parameter learning algorithms estimate node parameters from datasets through maximum likelihood estimation or Bayesian estimation methods. Inference algorithms compute conditional probabilities of target variables given observed evidence and network structure, employing exact methods (Variable Elimination, Junction Tree) or approximate techniques (Sampling, Loopy Belief Propagation) for efficient probability calculations.

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