Logo
latest

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.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.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.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- Graphical Models

  • Bayesian network
    • 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()
    • Tools for Bayesian networks
      • Generation of Bayesian network
        • BNGenerator
      • Generation of database
        • BNDatabaseGenerator
      • Comparison of Bayesian networks
        • ExactBNdistance
        • GibbsBNdistance
      • Explanation and analysis
        • JunctionTreeGenerator
        • EssentialGraph
        • MarkovBlanket
      • Fragment of Bayesian networks
        • BayesNetFragment
    • Inference
      • Exact Inference
        • Lazy Propagation
        • Shafer-Shenoy Inference
        • Variable Elimination
      • Approximated Inference
        • Loopy Belief Propagation
        • Sampling
        • Loopy 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()
  • 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.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()

3- Causality

  • pyAgrum.causal documentation
    • Causal Model
    • Causal Formula
    • Causal Inference
    • Other functions
    • Abstract Syntax Tree for Do-Calculus
    • Exceptions
    • Notebook’s tools for causality

4- scikit-learn-like BN Classifiers

  • pyAgrum.skbn documentation
    • Classifier using Bayesian networks
    • Discretizer for Bayesian networks

5- pyAgrum.lib modules

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

6- Miscellaneous

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

7- Customizing pyAgrum

  • Configuration for pyAgrum
pyAgrum
  • Bayesian network
  • Tools for Bayesian networks
  • Edit on GitLab
Previous Next

Tools for Bayesian networks

tools for comparing Bayesian networks

aGrUM/pyAgrum provide a set of classes and functions in order to easely work with Bayesian networks.

  • Generation of Bayesian network
    • BNGenerator
  • Generation of database
    • BNDatabaseGenerator
  • Comparison of Bayesian networks
    • ExactBNdistance
    • GibbsBNdistance
  • Explanation and analysis
    • JunctionTreeGenerator
    • EssentialGraph
    • MarkovBlanket
  • Fragment of Bayesian networks
    • BayesNetFragment

© Copyright 2018-22, aGrUM/pyAgrum Team <info_at_agrum_dot_org>. Revision f4672a6c. Last updated on Mar 18, 2023.

Built with Sphinx using a theme provided by Read the Docs.
Read the Docs v: latest
Versions
master
latest
1.7.0
1.6.1
1.6.0
1.5.2
1.5.1
1.5.0
1.4.1
1.4.0
1.3.2
1.3.1
1.3.0
1.2.0
1.1.1
1.1.0
1.0.0
0.22.9
0.22.8
0.22.7
0.22.5
0.22.4
0.22.3
0.22.2
0.22.0
0.21.0
0.20.3
0.20.2
0.20.1
0.19.3
0.19.2
0.19.1
0.19.0
0.18.2
0.18.1
0.18.0
0.17.3
0.17.2
0.17.1
0.17.0
0.16.4
0.16.3
0.16.2
0.16.1
0.15.0
0.14.3
0.13.6
readthedoc-newga
Downloads
On Read the Docs
Project Home
Builds