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Linkedin
Discord
Select theme
Dark
Light
Auto
Docs
Stats
Blog
News
aGrUM
Others
About us
Bibliography
Contributors
Contributing
Community
Publications
License
Support
FAQ
Getting Started
Installation
Tutorials
Reference
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 (BNM) model
8 - PyAgrum's Specific Features
SHAP values, SHALL values
Named tensors
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 The Book Of Why J Pearl 2018
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)
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Tutorials
1-Tutorials on pyAgrum
Section titled “1-Tutorials on pyAgrum”
01-tutorial_pyagrum
02-tutorial_using_pyagrum
2-Exact and Approximated Inference
Section titled “2-Exact and Approximated Inference”
41-inference_graphicalinference
42-inference_relevancereasoning
43-inference_lazypropagationadvancedfeatures
44-inference_approximateinference
45-inference_samplinginference
3-Learning Bayesian networks
Section titled “3-Learning Bayesian networks”
31-learning_structurallearning
32-learning_learningclassifier
33-learning_learningandessentialgraphs
34-learning_dirichletpriorandweigtheddatabase
35-learning_parametricem
36-learning_chi2andscoresfrombnlearner
4-Different Graphical Models
Section titled “4-Different Graphical Models”
21-models_influencediagram
22-models_dynamicbn
23-models_markovrandomfield
24-models_credalnetworks
25-models_o3prm
5-Bayesian networks as scikit-learn compliant classifiers
Section titled “5-Bayesian networks as scikit-learn compliant classifiers”
51-classifier_learning
52-classifier_discretization
53-classifier_compareclassifierswithsklearn
54-classifier_crossvalidation
55-classifier_binaryandnaryclassifierfrombn
6-Causal Bayesian Networks
Section titled “6-Causal Bayesian Networks”
61-causality_tobacco
62-causality_simpsonparadox
63-causality_multinomialsimpsonparadox
64-causality_docalculusexamples
65-causality_counterfactual
66-causality_causaleffectestimation
7-pyAgrum’s (experimental) models
Section titled “7-pyAgrum’s (experimental) models”
71-pymodels_ctbn
72-pymodels_clg
73-pymodels_bnmixture
8-pyAgrum’s specific features
Section titled “8-pyAgrum’s specific features”
92-tools-shapandshallvalues
93-tools_tensors
94-tools_aggregators
95-tools_explain
96-tools_klforbns
97-tools_comparingbn
98-tools_customizingandexportingbns
99-tools_configforpyagrum
9-Examples
Section titled “9-Examples”
11-examples_kaggletitanic
12-examples_naivecreditdefaultmodeling
13-examples_causalityandlearning
14-examples_sensitivityanalysisusingcredalnetworks
15-examples_quasicontinuous
16-examples_bayesianbetacoin
17-examples_causaleffectestimationfromdata
80-applications_ipywidgets
10-Examples from ‘The Book of Why’ (J. Pearl, 2018)
Section titled “10-Examples from ‘The Book of Why’ (J. Pearl, 2018)”
bow-c1p046-minituringtest
bow-c1p050-smallpox
bow-c3p115-airportbagexample
bow-c4p135-walkingexample
bow-c4p150-backdoorcriterion
bow-c5pxxx-smoking
bow-c6p178-montyhallproblem
bow-c7p213-docalculus
bow-c7p224-curiouscasesfordrsnow
bow-c7p229-goodandbadcholesterol
bow-c8p251-educationandexperience
