libDAI
Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
[detail level 123]
\NdaiNamespace for libDAI
 oCBBPCostFunctionPredefined cost functions that can be used with BBP
 oCBBPImplements BBP (Back-Belief-Propagation) [EaG09]
 |\CPropertiesParameters for BBP
 oCBipartiteGraphRepresents the neighborhood structure of nodes in an undirected, bipartite graph
 |\ClevelTypeUsed internally by isTree()
 oCBPApproximate inference algorithm "(Loopy) Belief Propagation"
 |oCEdgePropType used for storing edge properties
 |\CPropertiesParameters for BP
 oCBP_dualCalculates both types of BP messages and their normalizers from an InfAlg
 |oC_edges_tConvenience label for storing edge properties
 |oCbeliefsGroups together the data structures for storing the two types of beliefs and their normalizers
 |\CmessagesGroups together the data structures for storing the two types of messages and their normalizers
 oCCBPClass for CBP (Conditioned Belief Propagation) [EaG09]
 |\CPropertiesParameters for CBP
 oCClusterGraphA ClusterGraph is a hypergraph with variables as nodes, and "clusters" (sets of variables) as hyperedges
 oCsequentialVariableEliminationHelper object for dai::ClusterGraph::VarElim()
 oCgreedyVariableEliminationHelper object for dai::ClusterGraph::VarElim()
 oCDAGRepresents the neighborhood structure of nodes in a directed cyclic graph
 oCInfAlgInfAlg is an abstract base class, defining the common interface of all inference algorithms in libDAI
 oCDAIAlgCombines the abstract base class InfAlg with a graphical model (e.g., a FactorGraph or RegionGraph)
 oCDecMAPApproximate inference algorithm DecMAP, which constructs a MAP state by decimation
 |\CPropertiesParameters for DecMAP
 oCParameterEstimationBase class for parameter estimation methods
 oCCondProbEstimationEstimates the parameters of a conditional probability table, using pseudocounts
 oCSharedParametersRepresents a single factor or set of factors whose parameters should be estimated
 oCMaximizationStepA MaximizationStep groups together several parameter estimation tasks (SharedParameters objects) into a single unit
 oCEMAlgEMAlg performs Expectation Maximization to learn factor parameters
 oCEvidenceStores a data set consisting of multiple samples, where each sample is the observed joint state of some variables
 oCExactInfExact inference algorithm using brute force enumeration (mainly useful for testing purposes)
 |\CPropertiesParameters for ExactInf
 oCExceptionError handling in libDAI is done by throwing an instance of the Exception class
 oCTFactorRepresents a (probability) factor
 oCFactorGraphRepresents a factor graph
 oCTFactorSpRepresents a (probability) factor
 oCFBPApproximate inference algorithm "Fractional Belief Propagation" [WiH03]
 oCfo_idFunction object that returns the value itself
 oCfo_absFunction object that takes the absolute value
 oCfo_expFunction object that takes the exponent
 oCfo_logFunction object that takes the logarithm
 oCfo_log0Function object that takes the logarithm, except that log(0) is defined to be 0
 oCfo_invFunction object that takes the inverse
 oCfo_inv0Function object that takes the inverse, except that 1/0 is defined to be 0
 oCfo_plog0pFunction object that returns p*log0(p)
 oCfo_divides0Function object similar to std::divides(), but different in that dividing by zero results in zero
 oCfo_KLFunction object useful for calculating the KL distance
 oCfo_HellingerFunction object useful for calculating the Hellinger distance
 oCfo_powFunction object that returns x to the power y
 oCfo_maxFunction object that returns the maximum of two values
 oCfo_minFunction object that returns the minimum of two values
 oCfo_absdiffFunction object that returns the absolute difference of x and y
 oCGibbsApproximate inference algorithm "Gibbs sampling"
 |\CPropertiesParameters for Gibbs
 oCNeighborDescribes the neighbor relationship of two nodes in a graph
 oCGraphALRepresents the neighborhood structure of nodes in an undirected graph
 oCHAKApproximate inference algorithm: implementation of single-loop ("Generalized Belief Propagation") and double-loop algorithms by Heskes, Albers and Kappen [HAK03]
 |\CPropertiesParameters for HAK
 oCIndexForTool for looping over the states of several variables
 oCPermuteTool for calculating permutations of linear indices of multi-dimensional arrays
 oCmultiforMultifor makes it easy to perform a dynamic number of nested for loops
 oCStateMakes it easy to iterate over all possible joint states of variables within a VarSet
 oCJTreeExact inference algorithm using junction tree
 |\CPropertiesParameters for JTree
 oCLCApproximate inference algorithm "Loop Corrected Belief Propagation" [MoK07]
 |\CPropertiesParameters for LC
 oCMFApproximate inference algorithm "Mean Field"
 |\CPropertiesParameters for MF
 oCMRApproximate inference algorithm by Montanari and Rizzo [MoR05]
 |\CPropertiesParameters for MR
 oCTProbRepresents a vector with entries of type T
 oCTProbSpRepresents a vector with entries of type T
 oCPropertySetRepresents a set of properties, mapping keys (of type PropertyKey) to values (of type PropertyValue)
 oCRegionA Region is a set of variables with a counting number
 oCFRegionAn FRegion is a factor with a counting number
 oCRegionGraphA RegionGraph combines a bipartite graph consisting of outer regions (type FRegion) and inner regions (type Region) with a FactorGraph
 oCSmallSetRepresents a set; the implementation is optimized for a small number of elements
 oCfirst_lessFunction object that returns true if a.first < b.first
 oCTreeEPApproximate inference algorithm "Tree Expectation Propagation" [MiQ04]
 |oCPropertiesParameters for TreeEP
 |\CTreeEPSubTreeStores the data structures needed to efficiently update the approximation of an off-tree factor
 oCTRWBPApproximate inference algorithm "Tree-Reweighted Belief Propagation" [WJW03]
 oChash_mapHash_map is an alias for std::tr1::unordered_map
 oCVarRepresents a discrete random variable
 oCVarSetRepresents a set of variables
 oCDEdgeRepresents a directed edge
 oCUEdgeRepresents an undirected edge
 oCGraphELRepresents an undirected graph, implemented as a std::set of undirected edges
 oCWeightedGraphRepresents an undirected weighted graph, with weights of type T, implemented as a std::map mapping undirected edges to weights
 \CRootedTreeRepresents a rooted tree, implemented as a vector of directed edges