Creating Your First Bayesian Network in Python
Explore the foundational steps to create your first Bayesian network in Python. Learn to represent variables as nodes, define relationships with directed edges, and set conditional probability distributions. This lesson guides you through using the CausalNex library to model uncertainty and causal links between variables, providing a practical introduction to graphical models for probabilistic reasoning.
Our main goal in this lesson is to provide a high-level overview of the process of creating Bayesian networks using Python, laying the foundation for a deeper understanding in the subsequent lessons of this course.
Bayesian networks are a powerful tool for modeling uncertainty, representing probabilistic relationships between variables, and making inferences in complex domains. They have numerous applications in fields such as artificial intelligence, machine learning, statistics, and data science.
Create the network in Python using CausalNex
CausalNex is a Python library developed by QuantumBlack, a McKinsey company. It allows data scientists to utilize Bayesian networks for causal inference and probabilistic reasoning. Integrating traditional data science methods with graph theory, CausalNex offers tools for both ...