Tutorials and notebooks
gum.config
1- Fundamental components
Arc
Edge
DiGraph
DAG
UndiGraph
CliqueGraph
MixedGraph
MixedGraph.addArc()
MixedGraph.addEdge()
MixedGraph.addNode()
MixedGraph.addNodeWithId()
MixedGraph.addNodes()
MixedGraph.adjacents()
MixedGraph.arcs()
MixedGraph.boundary()
MixedGraph.chainComponent()
MixedGraph.children()
MixedGraph.clear()
MixedGraph.connectedComponents()
MixedGraph.edges()
MixedGraph.empty()
MixedGraph.emptyArcs()
MixedGraph.emptyEdges()
MixedGraph.eraseArc()
MixedGraph.eraseChildren()
MixedGraph.eraseEdge()
MixedGraph.eraseNeighbours()
MixedGraph.eraseNode()
MixedGraph.eraseParents()
MixedGraph.existsArc()
MixedGraph.existsEdge()
MixedGraph.existsNode()
MixedGraph.hasDirectedPath()
MixedGraph.hasMixedOrientedPath()
MixedGraph.hasUndirectedCycle()
MixedGraph.mixedOrientedPath()
MixedGraph.mixedUnorientedPath()
MixedGraph.neighbours()
MixedGraph.nodes()
MixedGraph.nodes2ConnectedComponent()
MixedGraph.parents()
MixedGraph.partialUndiGraph()
MixedGraph.size()
MixedGraph.sizeArcs()
MixedGraph.sizeEdges()
MixedGraph.toDot()
MixedGraph.topologicalOrder()
PDAG
PDAG.addArc()
PDAG.addEdge()
PDAG.addNode()
PDAG.addNodeWithId()
PDAG.addNodes()
PDAG.adjacents()
PDAG.arcs()
PDAG.boundary()
PDAG.cSeparation()
PDAG.chainComponent()
PDAG.children()
PDAG.clear()
PDAG.connectedComponents()
PDAG.edges()
PDAG.empty()
PDAG.emptyArcs()
PDAG.emptyEdges()
PDAG.eraseArc()
PDAG.eraseChildren()
PDAG.eraseEdge()
PDAG.eraseNeighbours()
PDAG.eraseNode()
PDAG.eraseParents()
PDAG.existsArc()
PDAG.existsEdge()
PDAG.existsNode()
PDAG.hasDirectedPath()
PDAG.hasMixedOrientedPath()
PDAG.hasMixedReallyOrientedPath()
PDAG.hasUndirectedCycle()
PDAG.mixedOrientedPath()
PDAG.mixedUnorientedPath()
PDAG.moralGraph()
PDAG.moralizedAncestralGraph()
PDAG.neighbours()
PDAG.nodes()
PDAG.nodes2ConnectedComponent()
PDAG.parents()
PDAG.partialUndiGraph()
PDAG.size()
PDAG.sizeArcs()
PDAG.sizeEdges()
PDAG.toDot()
PDAG.topologicalOrder()
DiscreteVariable
DiscreteVariable.asDiscretizedVar()
DiscreteVariable.asIntegerVar()
DiscreteVariable.asLabelizedVar()
DiscreteVariable.asNumericalDiscreteVar()
DiscreteVariable.asRangeVar()
DiscreteVariable.description()
DiscreteVariable.domain()
DiscreteVariable.domainSize()
DiscreteVariable.empty()
DiscreteVariable.index()
DiscreteVariable.label()
DiscreteVariable.labels()
DiscreteVariable.name()
DiscreteVariable.numerical()
DiscreteVariable.setDescription()
DiscreteVariable.setName()
DiscreteVariable.stype()
DiscreteVariable.toDiscretizedVar()
DiscreteVariable.toIntegerVar()
DiscreteVariable.toLabelizedVar()
DiscreteVariable.toNumericalDiscreteVar()
DiscreteVariable.toRangeVar()
DiscreteVariable.toStringWithDescription()
DiscreteVariable.varType()
LabelizedVariable
DiscretizedVariable
IntegerVariable
RangeVariable
NumericalDiscreteVariable
Instantiation
Instantiation.add()
Instantiation.addVarsFromModel()
Instantiation.chgVal()
Instantiation.clear()
Instantiation.contains()
Instantiation.dec()
Instantiation.decIn()
Instantiation.decNotVar()
Instantiation.decOut()
Instantiation.decVar()
Instantiation.domainSize()
Instantiation.empty()
Instantiation.end()
Instantiation.erase()
Instantiation.fromdict()
Instantiation.hamming()
Instantiation.inOverflow()
Instantiation.inc()
Instantiation.incIn()
Instantiation.incNotVar()
Instantiation.incOut()
Instantiation.incVar()
Instantiation.isMutable()
Instantiation.nbrDim()
Instantiation.pos()
Instantiation.rend()
Instantiation.reorder()
Instantiation.setFirst()
Instantiation.setFirstIn()
Instantiation.setFirstNotVar()
Instantiation.setFirstOut()
Instantiation.setFirstVar()
Instantiation.setLast()
Instantiation.setLastIn()
Instantiation.setLastNotVar()
Instantiation.setLastOut()
Instantiation.setLastVar()
Instantiation.setMutable()
Instantiation.setVals()
Instantiation.todict()
Instantiation.unsetEnd()
Instantiation.unsetOverflow()
Instantiation.val()
Instantiation.variable()
Instantiation.variablesSequence()
Potential
Potential.KL()
Potential.abs()
Potential.add()
Potential.argmax()
Potential.argmin()
Potential.contains()
Potential.domainSize()
Potential.draw()
Potential.empty()
Potential.entropy()
Potential.extract()
Potential.fillWith()
Potential.fillWithFunction()
Potential.findAll()
Potential.get()
Potential.inverse()
Potential.isNonZeroMap()
Potential.log2()
Potential.loopIn()
Potential.margMaxIn()
Potential.margMaxOut()
Potential.margMinIn()
Potential.margMinOut()
Potential.margProdIn()
Potential.margProdOut()
Potential.margSumIn()
Potential.margSumOut()
Potential.max()
Potential.maxNonOne()
Potential.min()
Potential.minNonZero()
Potential.names
Potential.nbrDim()
Potential.newFactory()
Potential.new_abs()
Potential.new_log2()
Potential.new_sq()
Potential.noising()
Potential.normalize()
Potential.normalizeAsCPT()
Potential.pos()
Potential.product()
Potential.putFirst()
Potential.random()
Potential.randomCPT()
Potential.randomDistribution()
Potential.remove()
Potential.reorganize()
Potential.scale()
Potential.set()
Potential.shape
Potential.sq()
Potential.sum()
Potential.thisown
Potential.toarray()
Potential.toclipboard()
Potential.tolatex()
Potential.tolist()
Potential.topandas()
Potential.translate()
Potential.var_dims
Potential.var_names
Potential.variable()
Potential.variablesSequence()
2- Graphical Models
BayesNet
BayesNet.add()
BayesNet.addAMPLITUDE()
BayesNet.addAND()
BayesNet.addArc()
BayesNet.addArcs()
BayesNet.addCOUNT()
BayesNet.addEXISTS()
BayesNet.addFORALL()
BayesNet.addLogit()
BayesNet.addMAX()
BayesNet.addMEDIAN()
BayesNet.addMIN()
BayesNet.addNoisyAND()
BayesNet.addNoisyOR()
BayesNet.addNoisyORCompound()
BayesNet.addNoisyORNet()
BayesNet.addOR()
BayesNet.addSUM()
BayesNet.addStructureListener()
BayesNet.addVariables()
BayesNet.addWeightedArc()
BayesNet.ancestors()
BayesNet.arcs()
BayesNet.beginTopologyTransformation()
BayesNet.changePotential()
BayesNet.changeVariableLabel()
BayesNet.changeVariableName()
BayesNet.check()
BayesNet.children()
BayesNet.clear()
BayesNet.completeInstantiation()
BayesNet.connectedComponents()
BayesNet.cpt()
BayesNet.dag()
BayesNet.descendants()
BayesNet.dim()
BayesNet.empty()
BayesNet.endTopologyTransformation()
BayesNet.erase()
BayesNet.eraseArc()
BayesNet.exists()
BayesNet.existsArc()
BayesNet.family()
BayesNet.fastPrototype()
BayesNet.generateCPT()
BayesNet.generateCPTs()
BayesNet.hasSameStructure()
BayesNet.idFromName()
BayesNet.ids()
BayesNet.isIndependent()
BayesNet.jointProbability()
BayesNet.loadBIF()
BayesNet.loadBIFXML()
BayesNet.loadDSL()
BayesNet.loadNET()
BayesNet.loadO3PRM()
BayesNet.loadUAI()
BayesNet.loadXDSL()
BayesNet.log10DomainSize()
BayesNet.log2JointProbability()
BayesNet.maxNonOneParam()
BayesNet.maxParam()
BayesNet.maxVarDomainSize()
BayesNet.minNonZeroParam()
BayesNet.minParam()
BayesNet.minimalCondSet()
BayesNet.moralGraph()
BayesNet.moralizedAncestralGraph()
BayesNet.names()
BayesNet.nodeId()
BayesNet.nodes()
BayesNet.nodeset()
BayesNet.parents()
BayesNet.reverseArc()
BayesNet.saveBIF()
BayesNet.saveBIFXML()
BayesNet.saveDSL()
BayesNet.saveNET()
BayesNet.saveO3PRM()
BayesNet.saveUAI()
BayesNet.saveXDSL()
BayesNet.size()
BayesNet.sizeArcs()
BayesNet.thisown
BayesNet.toDot()
BayesNet.topologicalOrder()
BayesNet.variable()
BayesNet.variableFromName()
BayesNet.variableNodeMap()
BNGenerator
BNDatabaseGenerator
ExactBNdistance
GibbsBNdistance
JunctionTreeGenerator
EssentialGraph
MarkovBlanket
BayesNetFragment
BNLearner
BNLearner.G2()
BNLearner.addForbiddenArc()
BNLearner.addMandatoryArc()
BNLearner.addPossibleEdge()
BNLearner.chi2()
BNLearner.correctedMutualInformation()
BNLearner.currentTime()
BNLearner.databaseWeight()
BNLearner.domainSize()
BNLearner.epsilon()
BNLearner.eraseForbiddenArc()
BNLearner.eraseMandatoryArc()
BNLearner.erasePossibleEdge()
BNLearner.fitParameters()
BNLearner.getNumberOfThreads()
BNLearner.hasMissingValues()
BNLearner.history()
BNLearner.idFromName()
BNLearner.isGumNumberOfThreadsOverriden()
BNLearner.latentVariables()
BNLearner.learnBN()
BNLearner.learnDAG()
BNLearner.learnEssentialGraph()
BNLearner.learnMixedGraph()
BNLearner.learnPDAG()
BNLearner.learnParameters()
BNLearner.logLikelihood()
BNLearner.maxIter()
BNLearner.maxTime()
BNLearner.messageApproximationScheme()
BNLearner.minEpsilonRate()
BNLearner.mutualInformation()
BNLearner.nameFromId()
BNLearner.names()
BNLearner.nbCols()
BNLearner.nbRows()
BNLearner.nbrIterations()
BNLearner.periodSize()
BNLearner.pseudoCount()
BNLearner.rawPseudoCount()
BNLearner.recordWeight()
BNLearner.score()
BNLearner.setDatabaseWeight()
BNLearner.setEpsilon()
BNLearner.setForbiddenArcs()
BNLearner.setInitialDAG()
BNLearner.setMandatoryArcs()
BNLearner.setMaxIndegree()
BNLearner.setMaxIter()
BNLearner.setMaxTime()
BNLearner.setMinEpsilonRate()
BNLearner.setNumberOfThreads()
BNLearner.setPeriodSize()
BNLearner.setPossibleEdges()
BNLearner.setPossibleSkeleton()
BNLearner.setRecordWeight()
BNLearner.setSliceOrder()
BNLearner.setVerbosity()
BNLearner.state()
BNLearner.use3off2()
BNLearner.useAprioriBDeu()
BNLearner.useAprioriDirichlet()
BNLearner.useAprioriSmoothing()
BNLearner.useBDeuPrior()
BNLearner.useDirichletPrior()
BNLearner.useEM()
BNLearner.useGreedyHillClimbing()
BNLearner.useK2()
BNLearner.useLocalSearchWithTabuList()
BNLearner.useMDLCorrection()
BNLearner.useMIIC()
BNLearner.useNMLCorrection()
BNLearner.useNoApriori()
BNLearner.useNoCorrection()
BNLearner.useNoPrior()
BNLearner.useScoreAIC()
BNLearner.useScoreBD()
BNLearner.useScoreBDeu()
BNLearner.useScoreBIC()
BNLearner.useScoreK2()
BNLearner.useScoreLog2Likelihood()
BNLearner.useSmoothingPrior()
BNLearner.verbosity()
InfluenceDiagram
InfluenceDiagram.add()
InfluenceDiagram.addArc()
InfluenceDiagram.addArcs()
InfluenceDiagram.addChanceNode()
InfluenceDiagram.addDecisionNode()
InfluenceDiagram.addStructureListener()
InfluenceDiagram.addUtilityNode()
InfluenceDiagram.addVariables()
InfluenceDiagram.ancestors()
InfluenceDiagram.arcs()
InfluenceDiagram.chanceNodeSize()
InfluenceDiagram.changeVariableName()
InfluenceDiagram.children()
InfluenceDiagram.clear()
InfluenceDiagram.completeInstantiation()
InfluenceDiagram.connectedComponents()
InfluenceDiagram.cpt()
InfluenceDiagram.dag()
InfluenceDiagram.decisionNodeSize()
InfluenceDiagram.decisionOrder()
InfluenceDiagram.decisionOrderExists()
InfluenceDiagram.descendants()
InfluenceDiagram.empty()
InfluenceDiagram.erase()
InfluenceDiagram.eraseArc()
InfluenceDiagram.exists()
InfluenceDiagram.existsArc()
InfluenceDiagram.existsPathBetween()
InfluenceDiagram.family()
InfluenceDiagram.fastPrototype()
InfluenceDiagram.getDecisionGraph()
InfluenceDiagram.hasSameStructure()
InfluenceDiagram.idFromName()
InfluenceDiagram.ids()
InfluenceDiagram.isChanceNode()
InfluenceDiagram.isDecisionNode()
InfluenceDiagram.isIndependent()
InfluenceDiagram.isUtilityNode()
InfluenceDiagram.loadBIFXML()
InfluenceDiagram.log10DomainSize()
InfluenceDiagram.moralGraph()
InfluenceDiagram.moralizedAncestralGraph()
InfluenceDiagram.names()
InfluenceDiagram.nodeId()
InfluenceDiagram.nodes()
InfluenceDiagram.nodeset()
InfluenceDiagram.parents()
InfluenceDiagram.saveBIFXML()
InfluenceDiagram.size()
InfluenceDiagram.sizeArcs()
InfluenceDiagram.thisown
InfluenceDiagram.toDot()
InfluenceDiagram.topologicalOrder()
InfluenceDiagram.utility()
InfluenceDiagram.utilityNodeSize()
InfluenceDiagram.variable()
InfluenceDiagram.variableFromName()
InfluenceDiagram.variableNodeMap()
ShaferShenoyLIMIDInference
ShaferShenoyLIMIDInference.MEU()
ShaferShenoyLIMIDInference.addEvidence()
ShaferShenoyLIMIDInference.addNoForgettingAssumption()
ShaferShenoyLIMIDInference.chgEvidence()
ShaferShenoyLIMIDInference.clear()
ShaferShenoyLIMIDInference.eraseAllEvidence()
ShaferShenoyLIMIDInference.eraseEvidence()
ShaferShenoyLIMIDInference.hardEvidenceNodes()
ShaferShenoyLIMIDInference.hasEvidence()
ShaferShenoyLIMIDInference.hasHardEvidence()
ShaferShenoyLIMIDInference.hasNoForgettingAssumption()
ShaferShenoyLIMIDInference.hasSoftEvidence()
ShaferShenoyLIMIDInference.influenceDiagram()
ShaferShenoyLIMIDInference.isSolvable()
ShaferShenoyLIMIDInference.junctionTree()
ShaferShenoyLIMIDInference.makeInference()
ShaferShenoyLIMIDInference.meanVar()
ShaferShenoyLIMIDInference.nbrEvidence()
ShaferShenoyLIMIDInference.nbrHardEvidence()
ShaferShenoyLIMIDInference.nbrSoftEvidence()
ShaferShenoyLIMIDInference.optimalDecision()
ShaferShenoyLIMIDInference.posterior()
ShaferShenoyLIMIDInference.posteriorUtility()
ShaferShenoyLIMIDInference.reducedGraph()
ShaferShenoyLIMIDInference.reducedLIMID()
ShaferShenoyLIMIDInference.reversePartialOrder()
ShaferShenoyLIMIDInference.setEvidence()
ShaferShenoyLIMIDInference.softEvidenceNodes()
ShaferShenoyLIMIDInference.updateEvidence()
CredalNet
CredalNet.NodeType_Credal
CredalNet.NodeType_Indic
CredalNet.NodeType_Precise
CredalNet.NodeType_Vacuous
CredalNet.addArc()
CredalNet.addVariable()
CredalNet.approximatedBinarization()
CredalNet.bnToCredal()
CredalNet.computeBinaryCPTMinMax()
CredalNet.credalNet_currentCpt()
CredalNet.credalNet_srcCpt()
CredalNet.currentNodeType()
CredalNet.current_bn()
CredalNet.domainSize()
CredalNet.epsilonMax()
CredalNet.epsilonMean()
CredalNet.epsilonMin()
CredalNet.fillConstraint()
CredalNet.fillConstraints()
CredalNet.get_binaryCPT_max()
CredalNet.get_binaryCPT_min()
CredalNet.hasComputedBinaryCPTMinMax()
CredalNet.idmLearning()
CredalNet.instantiation()
CredalNet.intervalToCredal()
CredalNet.intervalToCredalWithFiles()
CredalNet.isSeparatelySpecified()
CredalNet.lagrangeNormalization()
CredalNet.nodeType()
CredalNet.saveBNsMinMax()
CredalNet.setCPT()
CredalNet.setCPTs()
CredalNet.src_bn()
CNMonteCarloSampling
CNMonteCarloSampling.CN()
CNMonteCarloSampling.currentTime()
CNMonteCarloSampling.dynamicExpMax()
CNMonteCarloSampling.dynamicExpMin()
CNMonteCarloSampling.epsilon()
CNMonteCarloSampling.history()
CNMonteCarloSampling.insertEvidenceFile()
CNMonteCarloSampling.insertModalsFile()
CNMonteCarloSampling.makeInference()
CNMonteCarloSampling.marginalMax()
CNMonteCarloSampling.marginalMin()
CNMonteCarloSampling.maxIter()
CNMonteCarloSampling.maxTime()
CNMonteCarloSampling.messageApproximationScheme()
CNMonteCarloSampling.minEpsilonRate()
CNMonteCarloSampling.nbrIterations()
CNMonteCarloSampling.periodSize()
CNMonteCarloSampling.setEpsilon()
CNMonteCarloSampling.setMaxIter()
CNMonteCarloSampling.setMaxTime()
CNMonteCarloSampling.setMinEpsilonRate()
CNMonteCarloSampling.setPeriodSize()
CNMonteCarloSampling.setRepetitiveInd()
CNMonteCarloSampling.setVerbosity()
CNMonteCarloSampling.verbosity()
CNLoopyPropagation
CNLoopyPropagation.CN()
CNLoopyPropagation.InferenceType_nodeToNeighbours
CNLoopyPropagation.InferenceType_ordered
CNLoopyPropagation.InferenceType_randomOrder
CNLoopyPropagation.currentTime()
CNLoopyPropagation.dynamicExpMax()
CNLoopyPropagation.dynamicExpMin()
CNLoopyPropagation.epsilon()
CNLoopyPropagation.eraseAllEvidence()
CNLoopyPropagation.history()
CNLoopyPropagation.inferenceType()
CNLoopyPropagation.insertEvidenceFile()
CNLoopyPropagation.insertModalsFile()
CNLoopyPropagation.makeInference()
CNLoopyPropagation.marginalMax()
CNLoopyPropagation.marginalMin()
CNLoopyPropagation.maxIter()
CNLoopyPropagation.maxTime()
CNLoopyPropagation.messageApproximationScheme()
CNLoopyPropagation.minEpsilonRate()
CNLoopyPropagation.nbrIterations()
CNLoopyPropagation.periodSize()
CNLoopyPropagation.saveInference()
CNLoopyPropagation.setEpsilon()
CNLoopyPropagation.setMaxIter()
CNLoopyPropagation.setMaxTime()
CNLoopyPropagation.setMinEpsilonRate()
CNLoopyPropagation.setPeriodSize()
CNLoopyPropagation.setRepetitiveInd()
CNLoopyPropagation.setVerbosity()
CNLoopyPropagation.thisown
CNLoopyPropagation.verbosity()
MarkovRandomField
MarkovRandomField.add()
MarkovRandomField.addFactor()
MarkovRandomField.addStructureListener()
MarkovRandomField.addVariables()
MarkovRandomField.beginTopologyTransformation()
MarkovRandomField.changeVariableLabel()
MarkovRandomField.changeVariableName()
MarkovRandomField.clear()
MarkovRandomField.completeInstantiation()
MarkovRandomField.connectedComponents()
MarkovRandomField.dim()
MarkovRandomField.edges()
MarkovRandomField.empty()
MarkovRandomField.endTopologyTransformation()
MarkovRandomField.erase()
MarkovRandomField.eraseFactor()
MarkovRandomField.exists()
MarkovRandomField.existsEdge()
MarkovRandomField.factor()
MarkovRandomField.factors()
MarkovRandomField.fastPrototype()
MarkovRandomField.fromBN()
MarkovRandomField.generateFactor()
MarkovRandomField.generateFactors()
MarkovRandomField.graph()
MarkovRandomField.hasSameStructure()
MarkovRandomField.idFromName()
MarkovRandomField.ids()
MarkovRandomField.isIndependent()
MarkovRandomField.loadUAI()
MarkovRandomField.log10DomainSize()
MarkovRandomField.maxNonOneParam()
MarkovRandomField.maxParam()
MarkovRandomField.maxVarDomainSize()
MarkovRandomField.minNonZeroParam()
MarkovRandomField.minParam()
MarkovRandomField.minimalCondSet()
MarkovRandomField.names()
MarkovRandomField.neighbours()
MarkovRandomField.nodeId()
MarkovRandomField.nodes()
MarkovRandomField.nodeset()
MarkovRandomField.saveUAI()
MarkovRandomField.size()
MarkovRandomField.sizeEdges()
MarkovRandomField.smallestFactorFromNode()
MarkovRandomField.thisown
MarkovRandomField.toDot()
MarkovRandomField.toDotAsFactorGraph()
MarkovRandomField.variable()
MarkovRandomField.variableFromName()
MarkovRandomField.variableNodeMap()
ShaferShenoyMRFInference
PRMexplorer
PRMexplorer.aggType
PRMexplorer.classAggregates()
PRMexplorer.classAttributes()
PRMexplorer.classDag()
PRMexplorer.classImplements()
PRMexplorer.classParameters()
PRMexplorer.classReferences()
PRMexplorer.classSlotChains()
PRMexplorer.classes()
PRMexplorer.cpf()
PRMexplorer.getDirectSubClass()
PRMexplorer.getDirectSubInterfaces()
PRMexplorer.getDirectSubTypes()
PRMexplorer.getImplementations()
PRMexplorer.getLabelMap()
PRMexplorer.getLabels()
PRMexplorer.getSuperClass()
PRMexplorer.getSuperInterface()
PRMexplorer.getSuperType()
PRMexplorer.getalltheSystems()
PRMexplorer.interAttributes()
PRMexplorer.interReferences()
PRMexplorer.interfaces()
PRMexplorer.isAttribute()
PRMexplorer.isClass()
PRMexplorer.isInterface()
PRMexplorer.isType()
PRMexplorer.load()
PRMexplorer.types()
3- Causality
4- scikit-learn-like BN Classifiers
5- pyAgrum.lib modules
6- Miscellaneous
7- Customizing pyAgrum
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 random fields (a.k.a. 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