1-Fundamental components
2-Graphical Models
3-Causality
4-scikit-learn-like BN Classifiers
5-pyAgrum.lib modules
6-Miscellaneous
7-Customizing pyAgrum
Notebooks
gum.config
pyAgrum.causal.counterfactual
toDot()
pyagrum.ini
config
Tutorial pyAgrum
Using pyAgrum
Probablistic Inference with pyAgrum
Relevance Reasoning with pyAgrum
Some other features in Bayesian inference
Approximate inference in aGrUM (pyAgrum)
Different sampling inference
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
Influence diagram
dynamic Bayesian networks
Markov networks
Credal Networks
Object-Oriented Probabilistic Relational Model
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
Smoking, Cancer and causality
Simpson’s Paradox
Multinomial Simpson Paradox
Some examples of do-calculus
Counterfactual : the Effect of Education and Experience on Salary
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
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