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1.8.2

Tutorials and notebooks

  • Tutorials on pyAgrum
    • Tutorial pyAgrum
    • Using pyAgrum
  • Inference in Bayesian networks
    • 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
    • Fill Beta parameters with a re-parameterization
  • 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

1- Fundamental components

  • Graphs manipulation
    • Edges and Arcs
      • Arc
        • Arc
      • Edge
        • Edge
    • Directed Graphs
      • Digraph
        • DiGraph
      • Directed Acyclic Graph
        • DAG
    • Undirected Graphs
      • UndiGraph
        • UndiGraph
      • Clique Graph
        • CliqueGraph
    • Mixed Graph
      • 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()
    • Partially Directed Graph (DAG)
      • 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()
  • Random Variables
    • Common API for Random Discrete Variables
      • DiscreteVariable
        • DiscreteVariable.asDiscretizedVar()
        • DiscreteVariable.asIntegerVar()
        • DiscreteVariable.asLabelizedVar()
        • DiscreteVariable.asNumericalDiscreteVar()
        • DiscreteVariable.asRangeVar()
        • DiscreteVariable.belongs()
        • DiscreteVariable.closestIndex()
        • DiscreteVariable.closestLabel()
        • DiscreteVariable.description()
        • DiscreteVariable.domain()
        • DiscreteVariable.domainSize()
        • DiscreteVariable.empty()
        • DiscreteVariable.index()
        • DiscreteVariable.integerDomain()
        • DiscreteVariable.isEmpirical()
        • DiscreteVariable.isLabel()
        • DiscreteVariable.isTick()
        • DiscreteVariable.isValue()
        • DiscreteVariable.label()
        • DiscreteVariable.labels()
        • DiscreteVariable.maxVal()
        • DiscreteVariable.minVal()
        • DiscreteVariable.name()
        • DiscreteVariable.numerical()
        • DiscreteVariable.numericalDomain()
        • DiscreteVariable.posLabel()
        • DiscreteVariable.setDescription()
        • DiscreteVariable.setName()
        • DiscreteVariable.stype()
        • DiscreteVariable.tick()
        • DiscreteVariable.ticks()
        • DiscreteVariable.toDiscretizedVar()
        • DiscreteVariable.toFast()
        • DiscreteVariable.toIntegerVar()
        • DiscreteVariable.toLabelizedVar()
        • DiscreteVariable.toNumericalDiscreteVar()
        • DiscreteVariable.toRangeVar()
        • DiscreteVariable.toStringWithDescription()
        • DiscreteVariable.varType()
    • Concrete classes for Random Discrete Variables
      • LabelizedVariable
        • LabelizedVariable
      • DiscretizedVariable
        • DiscretizedVariable
      • IntegerVariable
        • IntegerVariable
      • RangeVariable
        • RangeVariable
      • NumericalDiscreteVariable
        • NumericalDiscreteVariable
  • Potential and Instantiation
    • Instantiation
      • 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
        • Potential.KL()
        • Potential.abs()
        • Potential.add()
        • Potential.argmax()
        • Potential.argmin()
        • Potential.contains()
        • Potential.domainSize()
        • Potential.draw()
        • Potential.empty()
        • Potential.entropy()
        • Potential.expectedValue()
        • 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_sgn()
        • 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.sgn()
        • 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- Bayesian networks

  • Model
    • 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()
  • Inference
    • Exact Inference
      • Lazy Propagation
        • LazyPropagation
      • Shafer-Shenoy Inference
        • ShaferShenoyInference
      • Variable Elimination
        • VariableElimination
    • Approximated Inference
      • Loopy Belief Propagation
        • LoopyBeliefPropagation
      • Sampling
        • Gibbs Sampling
        • Monte Carlo Sampling
        • Weighted Sampling
        • Importance Sampling
      • Loopy sampling
        • Loopy Gibbs Sampling
        • Loopy Monte Carlo Sampling
        • Loopy Weighted Sampling
        • Loopy Importance Sampling
  • Learning
    • 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()
  • -Tools for Bayesian networks
    • Random generation of Bayesian network
      • randomBN()
      • BNGenerator
        • BNGenerator.generate()
    • Generation of database
      • BNDatabaseGenerator
        • BNDatabaseGenerator.bn()
        • BNDatabaseGenerator.drawSamples()
        • BNDatabaseGenerator.log2likelihood()
        • BNDatabaseGenerator.samplesAt()
        • BNDatabaseGenerator.samplesLabelAt()
        • BNDatabaseGenerator.samplesNbCols()
        • BNDatabaseGenerator.samplesNbRows()
        • BNDatabaseGenerator.setAntiTopologicalVarOrder()
        • BNDatabaseGenerator.setRandomVarOrder()
        • BNDatabaseGenerator.setTopologicalVarOrder()
        • BNDatabaseGenerator.setVarOrder()
        • BNDatabaseGenerator.setVarOrderFromCSV()
        • BNDatabaseGenerator.toCSV()
        • BNDatabaseGenerator.to_pandas()
        • BNDatabaseGenerator.varOrder()
        • BNDatabaseGenerator.varOrderNames()
    • Information Theory of Bayesian network
      • InformationTheory
        • InformationTheory.entropyX()
        • InformationTheory.entropyXY()
        • InformationTheory.entropyXYgivenZ()
        • InformationTheory.entropyXgivenY()
        • InformationTheory.entropyY()
        • InformationTheory.entropyYgivenX()
        • InformationTheory.mutualInformationXY()
        • InformationTheory.mutualInformationXYgivenZ()
        • InformationTheory.variationOfInformationXY()
    • Comparison of Bayesian networks
      • ExactBNdistance
        • ExactBNdistance.compute()
      • GibbsBNdistance
        • GibbsBNdistance.burnIn()
        • GibbsBNdistance.compute()
        • GibbsBNdistance.continueApproximationScheme()
        • GibbsBNdistance.currentTime()
        • GibbsBNdistance.disableEpsilon()
        • GibbsBNdistance.disableMaxIter()
        • GibbsBNdistance.disableMaxTime()
        • GibbsBNdistance.disableMinEpsilonRate()
        • GibbsBNdistance.enableEpsilon()
        • GibbsBNdistance.enableMaxIter()
        • GibbsBNdistance.enableMaxTime()
        • GibbsBNdistance.enableMinEpsilonRate()
        • GibbsBNdistance.epsilon()
        • GibbsBNdistance.history()
        • GibbsBNdistance.initApproximationScheme()
        • GibbsBNdistance.isDrawnAtRandom()
        • GibbsBNdistance.isEnabledEpsilon()
        • GibbsBNdistance.isEnabledMaxIter()
        • GibbsBNdistance.isEnabledMaxTime()
        • GibbsBNdistance.isEnabledMinEpsilonRate()
        • GibbsBNdistance.maxIter()
        • GibbsBNdistance.maxTime()
        • GibbsBNdistance.messageApproximationScheme()
        • GibbsBNdistance.minEpsilonRate()
        • GibbsBNdistance.nbrDrawnVar()
        • GibbsBNdistance.nbrIterations()
        • GibbsBNdistance.periodSize()
        • GibbsBNdistance.remainingBurnIn()
        • GibbsBNdistance.setBurnIn()
        • GibbsBNdistance.setDrawnAtRandom()
        • GibbsBNdistance.setEpsilon()
        • GibbsBNdistance.setMaxIter()
        • GibbsBNdistance.setMaxTime()
        • GibbsBNdistance.setMinEpsilonRate()
        • GibbsBNdistance.setNbrDrawnVar()
        • GibbsBNdistance.setPeriodSize()
        • GibbsBNdistance.setVerbosity()
        • GibbsBNdistance.startOfPeriod()
        • GibbsBNdistance.stateApproximationScheme()
        • GibbsBNdistance.stopApproximationScheme()
        • GibbsBNdistance.updateApproximationScheme()
        • GibbsBNdistance.verbosity()
    • Explanation and analysis
      • JunctionTreeGenerator
        • JunctionTreeGenerator.binaryJoinTree()
        • JunctionTreeGenerator.eliminationOrder()
        • JunctionTreeGenerator.junctionTree()
      • EssentialGraph
        • EssentialGraph.arcs()
        • EssentialGraph.children()
        • EssentialGraph.connectedComponents()
        • EssentialGraph.edges()
        • EssentialGraph.neighbours()
        • EssentialGraph.nodes()
        • EssentialGraph.parents()
        • EssentialGraph.pdag()
        • EssentialGraph.size()
        • EssentialGraph.sizeArcs()
        • EssentialGraph.sizeEdges()
        • EssentialGraph.sizeNodes()
        • EssentialGraph.skeleton()
        • EssentialGraph.toDot()
      • MarkovBlanket
        • MarkovBlanket.arcs()
        • MarkovBlanket.children()
        • MarkovBlanket.connectedComponents()
        • MarkovBlanket.dag()
        • MarkovBlanket.hasSameStructure()
        • MarkovBlanket.nodes()
        • MarkovBlanket.parents()
        • MarkovBlanket.size()
        • MarkovBlanket.sizeArcs()
        • MarkovBlanket.sizeNodes()
        • MarkovBlanket.toDot()
    • Fragment of Bayesian networks
      • BayesNetFragment
        • BayesNetFragment.addArcs()
        • BayesNetFragment.addStructureListener()
        • BayesNetFragment.addVariables()
        • BayesNetFragment.ancestors()
        • BayesNetFragment.arcs()
        • BayesNetFragment.check()
        • BayesNetFragment.checkConsistency()
        • BayesNetFragment.children()
        • BayesNetFragment.completeInstantiation()
        • BayesNetFragment.connectedComponents()
        • BayesNetFragment.cpt()
        • BayesNetFragment.dag()
        • BayesNetFragment.descendants()
        • BayesNetFragment.dim()
        • BayesNetFragment.empty()
        • BayesNetFragment.exists()
        • BayesNetFragment.existsArc()
        • BayesNetFragment.family()
        • BayesNetFragment.hasSameStructure()
        • BayesNetFragment.idFromName()
        • BayesNetFragment.ids()
        • BayesNetFragment.installAscendants()
        • BayesNetFragment.installCPT()
        • BayesNetFragment.installMarginal()
        • BayesNetFragment.installNode()
        • BayesNetFragment.isIndependent()
        • BayesNetFragment.isInstalledNode()
        • BayesNetFragment.jointProbability()
        • BayesNetFragment.log10DomainSize()
        • BayesNetFragment.log2JointProbability()
        • BayesNetFragment.maxNonOneParam()
        • BayesNetFragment.maxParam()
        • BayesNetFragment.maxVarDomainSize()
        • BayesNetFragment.minNonZeroParam()
        • BayesNetFragment.minParam()
        • BayesNetFragment.minimalCondSet()
        • BayesNetFragment.moralGraph()
        • BayesNetFragment.moralizedAncestralGraph()
        • BayesNetFragment.names()
        • BayesNetFragment.nodeId()
        • BayesNetFragment.nodes()
        • BayesNetFragment.nodeset()
        • BayesNetFragment.parents()
        • BayesNetFragment.property()
        • BayesNetFragment.propertyWithDefault()
        • BayesNetFragment.setProperty()
        • BayesNetFragment.size()
        • BayesNetFragment.sizeArcs()
        • BayesNetFragment.toBN()
        • BayesNetFragment.toDot()
        • BayesNetFragment.topologicalOrder()
        • BayesNetFragment.uninstallCPT()
        • BayesNetFragment.uninstallNode()
        • BayesNetFragment.variable()
        • BayesNetFragment.variableFromName()
        • BayesNetFragment.variableNodeMap()
        • BayesNetFragment.whenArcAdded()
        • BayesNetFragment.whenArcDeleted()
        • BayesNetFragment.whenNodeAdded()
        • BayesNetFragment.whenNodeDeleted()
  • Causality
    • Causal Model
      • CausalModel
        • CausalModel.addCausalArc()
        • CausalModel.addLatentVariable()
        • CausalModel.arcs()
        • CausalModel.backDoor()
        • CausalModel.causalBN()
        • CausalModel.children()
        • CausalModel.eraseCausalArc()
        • CausalModel.existsArc()
        • CausalModel.frontDoor()
        • CausalModel.idFromName()
        • CausalModel.latentVariablesIds()
        • CausalModel.names()
        • CausalModel.nodes()
        • CausalModel.observationalBN()
        • CausalModel.parents()
        • CausalModel.toDot()
    • Causal Formula
      • CausalFormula
        • CausalFormula.cm
        • CausalFormula.copy()
        • CausalFormula.eval()
        • CausalFormula.latexQuery()
        • CausalFormula.root
        • CausalFormula.toLatex()
    • Causal Inference
      • causalImpact()
      • doCalculusWithObservation()
      • identifyingIntervention()
    • Other functions
      • backdoor_generator()
      • frontdoor_generator()
    • Abstract Syntax Tree for Do-Calculus
      • Internal node structure
        • ASTtree
        • ASTBinaryOp
      • Basic Binary Operations
        • ASTplus
        • ASTminus
        • ASTdiv
        • ASTmult
      • Complex operations
        • ASTsum
        • ASTjointProba
        • ASTposteriorProba
    • Exceptions
      • HedgeException
        • HedgeException.args
      • UnidentifiableException
        • UnidentifiableException.args
    • Notebook’s tools for causality
      • getCausalImpact()
      • getCausalModel()
      • showCausalImpact()
      • showCausalModel()
  • Classifier
    • Classifier using Bayesian networks
      • BNClassifier
        • BNClassifier.XYfromCSV()
        • BNClassifier.fit()
        • BNClassifier.fromTrainedModel()
        • BNClassifier.get_params()
        • BNClassifier.predict()
        • BNClassifier.predict_proba()
        • BNClassifier.preparedData()
        • BNClassifier.score()
        • BNClassifier.set_params()
        • BNClassifier.showROC_PR()
    • Discretizer for Bayesian networks
      • BNDiscretizer
        • BNDiscretizer.audit()
        • BNDiscretizer.clear()
        • BNDiscretizer.createVariable()
        • BNDiscretizer.discretizationCAIM()
        • BNDiscretizer.discretizationElbowMethodRotation()
        • BNDiscretizer.discretizationMDLP()
        • BNDiscretizer.discretizationNML()
        • BNDiscretizer.setDiscretizationParameters()

3- Other graphical models

  • Influence Diagram
    • Model for Decision in PGM
      • 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()
    • Inference for Influence Diagram
      • 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()
  • Credal Network
    • CN Model
      • 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()
    • CN Inference
      • 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()
  • Markov random field
    • Undirected Graphical Model
      • 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.family()
        • 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()
    • Inference in Markov random fields
      • Shafer-Shenoy Inference in Markov random field
        • ShaferShenoyMRFInference
  • Probabilistic Relational Models
    • 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()

4- pyAgrum.lib modules

  • pyAgrum.lib.notebook
  • pyAgrum.lib.image
  • pyAgrum.lib.explain
  • pyAgrum.lib.dynamicBN
  • other pyAgrum.lib modules

5- Miscellaneous

  • Random numbers from aGrUM
  • fast syntax
  • Functions from pyAgrum
  • Listeners
  • Exceptions from aGrUM

6- Customizing pyAgrum

  • Configuration for pyAgrum
pyAgrum
  • Search

© Copyright 2018-23, aGrUM/pyAgrum Team <info_at_agrum_dot_org> (pyAgrum re1.8.2). Revision d76c830b. Last updated on Jun 01, 2023.

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