Tutorials

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1-Tutorials on pyAgrum

Tutorial pyAgrum

Using pyAgrum

2-Exact and Approximated Inference

Probablistic Inference with pyAgrum

Relevance Reasoning with pyAgrum

Some other features in Bayesian inference

Approximate inference in aGrUM (pyAgrum)

Different sampling inference

3-Learning Bayesian networks

Learning the structure of a Bayesian network

Learning BN as probabilistic classifier

Learning essential graphs

Dirichlet prior

Parametric EM (missing data)

Scores, Chi2, etc. with BNLearner

4-Different Graphical Models

Influence diagram

Dynamic Bayesian Networks

Markov random fields (a.k.a. Markov Networks)

Credal Networks

Object-Oriented Probabilistic Relational Model

5-Bayesian networks as scikit-learn compliant classifiers

Learning classifiers

Discretization using pyAgrum's DiscreteTypeProcessor

Comparing classifiers (including Bayesian networks) with scikit-learn

Using sklearn to cross-validate bayesian network classifier

From a Bayesian network to a Classifier

6-Causal Bayesian Networks

Smoking, Cancer and causality

Simpson's Paradox

Multinomial Simpson Paradox

Some examples of do-calculus

Counterfactual : the Effect of Education and Experience on Salary

Causal Effect Estimation in datasets

7-pyAgrum’s (experimental) models

Using Continuous-Time Bayesian Networks

Conditional Linear Gaussian models

Bayesian Network Mixture (gbnm) model

Quantum Bayesian Network Sampling (`pyagrum.qBNSampling`)

8-pyAgrum’s specific features

Loading and saving graphical models

SHAP values, SHALL values

Tensors and graphs

Aggregators

Explaining a model

Kullback-Leibler for Bayesian networks

Comparing BNs

Customizing and exporting graphical models and CPTs as image (pdf, png)

`gum.config` :the configuration object for pyAgrum

9-Examples

Kaggle Titanic

Naive modeling of credit defaults using a Markov Random Field

Learning and causality

Sensitivity analysis for Bayesian networks using credal networks

Quasi-continuous BN

Bayesian Beta Distributed Coin Inference

ACE estimations from real observational data

interactive notebooks

10-Examples from :cite:t:pearl2018why

MiniTuring (p46)

Smallpox Paradox (p50)

Where is my Bag ? (p115)

Walking Example (p135)

Back-Door Criterion (p150)

Smoking (chapter 5)

Monty Hall Problem (p178)

Do-Calculus (p213)

The Curious Case(s) For Dr. Snow (p224)

Good and Bas Cholesterol (p229)

The Effect of Education and Experience on Salary (p251)