David5971/Seminar-3-Exercise1: v1.0.0:ML_BayesianAnalysis
Abstract
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: Network Instantiation & Visualization Data Simulation D-Separation & Independencies Markov Blanket Analysis Probabilistic Inference Diagnostic Reasoning Causal Reasoning Intercausal Reasoning (Explaining Away) Exact vs. Approximate Inference
Fields of Study & Subjects
Bayesian Modeling and Causal Inference
Gaussian Processes and Bayesian Inference
Logic
Reasoning
and Knowledge
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 27 February 2026
- Language
- EN