David5971/Seminar-3-Exercise1: v1.0.0:ML_BayesianAnalysis

This repository contains a Jupyter Notebook that explores the fundamentals and advanced concepts of Bayesian Networks using Python's pgmpy library. Utilizing the 'Child' medical diagnostic model.

The notebook walks through the entire pipeline of working with a Bayesian Network, from structural visualization and data simulation to complex probabilistic reasoning. Key Features & Concepts Covered:

1. Network Instantiation & Visualization
2. Data Simulation
3. D-Separation & Independencies
4. Markov Blanket Analysis
5. Probabilistic Inference
6. Diagnostic Reasoning
7. Causal Reasoning
8. Intercausal Reasoning (Explaining Away)
9. Exact vs. Approximate Inference