David5971/Seminar-3: Bayesian Network Analysis and Inference
Abstract
This repository contains a Jupyter Notebook that explores the fundamentals and advanced concepts of Bayesian Networks. Using 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 (Exact) 6. Diagnostic Reasoning 7. Causal Reasoning 8. Intercausal Reasoning (Explaining Away) 9. 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