A Simple Model: From Causality to Bayesian Networks

Bayesian networks are powerful probabilistic graphical models that capture the dependencies between random variables and their conditional probabilities. However, a common challenge arises when it comes to manually inputting the conditional probabilities into Conditional Probability Distribution (CPD) tables, especially when working with complex networks. This problem becomes even more apparent when attempting to implement the Bayesian network using NetworkX.

To address this issue, we will explore a data-driven approach to constructing BN. By leveraging data collected through experiments, observational studies, or even simulating the data, we can estimate the underlying conditional probabilities and create the BN with greater ease and efficiency.

The rain model from a data perspective

The objective of this lesson is to create a BN using Python.

Step 1: Identify the random variables

Let's draw the next causal model:

• We have four random variables: Cloudy, Sprinkler, Rain, Wet Grass.

• Each variable can take on two states, True or False.

• Each variable should be represented by a node in the Bayesian network.