Class List

Here are the classes, structs, unions and interfaces with brief descriptions:
dai::BBPImplements BBP (Back-Belief-Propagation) [EaG09]
dai::BBP::PropertiesParameters for BBP
dai::BBPCostFunctionPredefined cost functions that can be used with BBP
dai::BipartiteGraphRepresents the neighborhood structure of nodes in an undirected, bipartite graph
dai::BipartiteGraph::levelTypeUsed internally by isTree()
dai::BPApproximate inference algorithm "(Loopy) Belief Propagation"
dai::BP::EdgePropType used for storing edge properties
dai::BP::PropertiesParameters for BP
dai::BP_dualCalculates both types of BP messages and their normalizers from an InfAlg
dai::BP_dual::_edges_t< T >Convenience label for storing edge properties
dai::BP_dual::beliefsGroups together the data structures for storing the two types of beliefs and their normalizers
dai::BP_dual::messagesGroups together the data structures for storing the two types of messages and their normalizers
dai::CBPClass for CBP (Conditioned Belief Propagation) [EaG09]
dai::CBP::PropertiesParameters for CBP
dai::ClusterGraphA ClusterGraph is a hypergraph with variables as nodes, and "clusters" (sets of variables) as hyperedges
dai::CondProbEstimationEstimates the parameters of a conditional probability table, using pseudocounts
dai::DAGRepresents the neighborhood structure of nodes in a directed cyclic graph
dai::DAIAlg< GRM >Combines the abstract base class InfAlg with a graphical model (e.g., a FactorGraph or RegionGraph)
dai::DecMAPApproximate inference algorithm DecMAP, which constructs a MAP state by decimation
dai::DecMAP::PropertiesParameters for DecMAP
dai::DEdgeRepresents a directed edge
dai::EMAlgEMAlg performs Expectation Maximization to learn factor parameters
dai::EvidenceStores a data set consisting of multiple samples, where each sample is the observed joint state of some variables
dai::ExactInfExact inference algorithm using brute force enumeration (mainly useful for testing purposes)
dai::ExactInf::PropertiesParameters for ExactInf
dai::ExceptionError handling in libDAI is done by throwing an instance of the Exception class
dai::FactorGraphRepresents a factor graph
dai::FBPApproximate inference algorithm "Fractional Belief Propagation" [WiH03]
dai::fo_abs< T >Function object that takes the absolute value
dai::fo_absdiff< T >Function object that returns the absolute difference of x and y
dai::fo_divides0< T >Function object similar to std::divides(), but different in that dividing by zero results in zero
dai::fo_exp< T >Function object that takes the exponent
dai::fo_Hellinger< T >Function object useful for calculating the Hellinger distance
dai::fo_id< T >Function object that returns the value itself
dai::fo_inv< T >Function object that takes the inverse
dai::fo_inv0< T >Function object that takes the inverse, except that 1/0 is defined to be 0
dai::fo_KL< T >Function object useful for calculating the KL distance
dai::fo_log< T >Function object that takes the logarithm
dai::fo_log0< T >Function object that takes the logarithm, except that log(0) is defined to be 0
dai::fo_max< T >Function object that returns the maximum of two values
dai::fo_min< T >Function object that returns the minimum of two values
dai::fo_plog0p< T >Function object that returns p*log0(p)
dai::fo_pow< T >Function object that returns x to the power y
dai::FRegionAn FRegion is a factor with a counting number
dai::GibbsApproximate inference algorithm "Gibbs sampling"
dai::Gibbs::PropertiesParameters for Gibbs
dai::GraphALRepresents the neighborhood structure of nodes in an undirected graph
dai::GraphELRepresents an undirected graph, implemented as a std::set of undirected edges
dai::greedyVariableEliminationHelper object for dai::ClusterGraph::VarElim()
dai::HAKApproximate inference algorithm: implementation of single-loop ("Generalized Belief Propagation") and double-loop algorithms by Heskes, Albers and Kappen [HAK03]
dai::HAK::PropertiesParameters for HAK
dai::hash_map< T, U, H >Hash_map is an alias for std::tr1::unordered_map
dai::IndexForTool for looping over the states of several variables
dai::InfAlgInfAlg is an abstract base class, defining the common interface of all inference algorithms in libDAI
dai::JTreeExact inference algorithm using junction tree
dai::JTree::PropertiesParameters for JTree
dai::LCApproximate inference algorithm "Loop Corrected Belief Propagation" [MoK07]
dai::LC::PropertiesParameters for LC
dai::MaximizationStepA MaximizationStep groups together several parameter estimation tasks (SharedParameters objects) into a single unit
dai::MFApproximate inference algorithm "Mean Field"
dai::MF::PropertiesParameters for MF
dai::MRApproximate inference algorithm by Montanari and Rizzo [MoR05]
dai::MR::PropertiesParameters for MR
dai::multiforMultifor makes it easy to perform a dynamic number of nested for loops
dai::NeighborDescribes the neighbor relationship of two nodes in a graph
dai::ParameterEstimationBase class for parameter estimation methods
dai::PermuteTool for calculating permutations of linear indices of multi-dimensional arrays
dai::PropertySetRepresents a set of properties, mapping keys (of type PropertyKey) to values (of type PropertyValue)
dai::RegionA Region is a set of variables with a counting number
dai::RegionGraphA RegionGraph combines a bipartite graph consisting of outer regions (type FRegion) and inner regions (type Region) with a FactorGraph
dai::RootedTreeRepresents a rooted tree, implemented as a vector of directed edges
dai::sequentialVariableEliminationHelper object for dai::ClusterGraph::VarElim()
dai::SharedParametersRepresents a single factor or set of factors whose parameters should be estimated
dai::SmallSet< T >Represents a set; the implementation is optimized for a small number of elements
dai::StateMakes it easy to iterate over all possible joint states of variables within a VarSet
dai::TFactor< T >Represents a (probability) factor
dai::TProb< T >Represents a vector with entries of type T
dai::TreeEPApproximate inference algorithm "Tree Expectation Propagation" [MiQ04]
dai::TreeEP::PropertiesParameters for TreeEP
dai::TreeEP::TreeEPSubTreeStores the data structures needed to efficiently update the approximation of an off-tree factor
dai::TRWBPApproximate inference algorithm "Tree-Reweighted Belief Propagation" [WJW03]
dai::UEdgeRepresents an undirected edge
dai::VarRepresents a discrete random variable
dai::VarSetRepresents a set of variables
dai::WeightedGraph< T >Represents an undirected weighted graph, with weights of type T, implemented as a std::map mapping undirected edges to weights

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