Markov random field

A Markov random field is a undirected probabilistic graphical model. It represents a joint distribution over a set of random variables. In pyAgrum, the variables are (for now) only discrete.

A Markov random field uses a undirected graph to represent conditional independence in the joint distribution. These conditional independence allow to factorize the joint distribution, thereby allowing to compactly represent very large ones.

\[P(X_1,\cdots,X_n)\propto\prod_{i=1}^{n_c} \phi_i(C_i)\]

Where the \(\phi_i\) are potentials over the \(n_c\) cliques of the undirected graph.

Moreover, inference algorithms can also use this graph to speed up the computations.

Note

Markov Random Field are alse called Markov Network. After tag 1.5.2, pyAgrum uses the terminology Markov Random Field.

Tutorial

• Tutorial on Markov random field

Reference

• Undirected Graphical Model
  • MarkovRandomField
    • MarkovRandomField.add()
    • MarkovRandomField.addFactor()
    • MarkovRandomField.addStructureListener()
    • MarkovRandomField.addVariables()
    • MarkovRandomField.adjacencyMatrix()
    • 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.properties()
    • MarkovRandomField.saveUAI()
    • MarkovRandomField.size()
    • MarkovRandomField.sizeEdges()
    • MarkovRandomField.smallestFactorFromNode()
    • MarkovRandomField.thisown
    • MarkovRandomField.toDot()
    • MarkovRandomField.toDotAsFactorGraph()
    • MarkovRandomField.toFast()
    • MarkovRandomField.variable()
    • MarkovRandomField.variableFromName()
    • MarkovRandomField.variableNodeMap()
• Inference in Markov random fields
  • Shafer-Shenoy Inference in Markov random field
    • ShaferShenoyMRFInference