Learning Bayesian Networks
Learning a Bayesian network involves two main tasks:
- Structure Learning: Determining the network structure (i.e., the DAG).
- Parameter Learning: Estimating the conditional probability distributions.
Structure learning can be done through algorithms that search for the best structure given the data, while parameter learning typically uses methods such as Maximum Likelihood Estimation (MLE) or Bayesian Estimation.
Understanding Bayesian Networks: Modeling Probabilistic Relationships Between Variables
Bayesian networks, also known as belief networks or Bayesian belief networks (BBNs), are powerful tools for representing and reasoning about uncertain knowledge. These networks use a graphical structure to encode probabilistic relationships among variables, making them invaluable in fields such as artificial intelligence, bioinformatics, and decision analysis.
This article delves into how Bayesian networks model probabilistic relationships between variables, covering their structure, conditional independence, joint probability distribution, inference, learning, and applications.
Table of Content
- Basic Structure of Bayesian Networks
- Conditional Independence
- Joint Probability Distribution
- Inference in Bayesian Networks
- Learning Bayesian Networks
- Interview Question: “How Do Bayesian Networks Model Probabilistic Relationships Between Variables?”