Tutorials on pyAgrum

Tutorial pyAgrum

Using pyAgrum

Exact and Approxiomated Inference

Probablistic Inference with pyAgrum

Relevance Reasoning with pyAgrum

Some other features in Bayesian inference

Approximate inference in aGrUM (pyAgrum)

Different sampling inference

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

Different Graphical Models

Influence diagram

Dynamic Bayesian Networks

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

Credal Networks

Object-Oriented Probabilistic Relational Model

Bayesian networks as scikit-learn compliant classifiers

Learning classifiers

The BNDiscretizer Class

Comparing classifiers (including Bayesian networks) with scikit-learn

Using sklearn to cross-validate bayesian network classifier

From a Bayesian network to a Classifier

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

Examples

Asthma

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

Parameter learning with Pandas

Bayesian Beta Distributed Coin Inference

interactive notebooks

pyAgrum’s specific features

Potentials : named tensors

Aggregators

Explaining a model

Kullback-Leibler for Bayesian networks

Comparing BNs

Coloring and exporting graphical models as image (pdf, png)

gum.config :the configuration object for pyAgrum