Bayesian Networks can feel confusing because they combine two things at once.

Graphs show structure.

Probabilities show uncertainty.

The key is to see them as one model, not two separate topics.

Core Idea

A Bayesian Network represents relationships between variables using a directed graph.

Each node is a variable.

Each edge shows a dependency.

Each node also has probability values that explain how it behaves under different conditions.

So the model is not just a diagram.

It is a structured probability system.

The Key Structure

A Bayesian Network is built from two main parts:

Graph structure + probability tables

More specifically:

DAG + CPT = Bayesian Network

Where:

DAG = Directed Acyclic Graph

CPT = Conditional Probability Table

The DAG tells you which variables depend on which other variables.

The CPT tells you the actual probability values for those dependencies.

Implementation View

At a high level, building a Bayesian Network looks like this:

define variables as nodes

define…