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

In this article, we will conduct an in-depth exploration of three core aspects of Bayesian networks: structure learning, parameter learning, and inference. Structure learning involves analyzing relationships between variables in datasets to construct optimal Bayesian network models, typically implemented using algorithms like PC, Greedy Hill-Climbing, or Tabu Search that iteratively evaluate conditional dependencies. Parameter learning focuses on estimating node parameters from existing data using methods such as Maximum Likelihood Estimation (MLE) or Bayesian Estimation with Dirichlet priors to enhance model accuracy. Inference utilizes the constructed network model and observed evidence to compute probability distributions for unknown variables through algorithms like Variable Elimination, Belief Propagation, or Markov Chain Monte Carlo (MCMC) sampling. These three components are interdependent - only through their coordinated operation can we build an efficient and accurate Bayesian network system capable of handling real-world probabilistic reasoning tasks.