 dai::BP_dual::_edges_t< T > | Convenience label for storing edge properties |
 dai::BBP | Implements BBP (Back-Belief-Propagation) [EaG09] |
 dai::BBPCostFunction | Predefined cost functions that can be used with BBP |
 dai::BP_dual::beliefs | Groups together the data structures for storing the two types of beliefs and their normalizers |
 dai::BipartiteGraph | Represents the neighborhood structure of nodes in an undirected, bipartite graph |
 dai::BP_dual | Calculates both types of BP messages and their normalizers from an InfAlg |
 dai::ClusterGraph | A ClusterGraph is a hypergraph with variables as nodes, and "clusters" (sets of variables) as hyperedges |
 dai::DAG | Represents the neighborhood structure of nodes in a directed cyclic graph |
 dai::DEdge | Represents a directed edge |
 dai::BP::EdgeProp | Type used for storing edge properties |
 dai::EMAlg | EMAlg performs Expectation Maximization to learn factor parameters |
 dai::Evidence | Stores a data set consisting of multiple samples, where each sample is the observed joint state of some variables |
 dai::Exception | Error handling in libDAI is done by throwing an instance of the Exception class |
 dai::FactorGraph | Represents a factor graph |
  dai::RegionGraph | A RegionGraph combines a bipartite graph consisting of outer regions (type FRegion) and inner regions (type Region) with a FactorGraph |
 dai::first_less< T1, T2 > | Function object that returns true if a.first < b.first |
 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::GraphAL | Represents the neighborhood structure of nodes in an undirected graph |
 dai::GraphEL | Represents an undirected graph, implemented as a std::set of undirected edges |
 dai::greedyVariableElimination | Helper object for dai::ClusterGraph::VarElim() |
 dai::hash_map< T, U, H > | Hash_map is an alias for std::tr1::unordered_map |
 dai::IndexFor | Tool for looping over the states of several variables |
 dai::InfAlg | InfAlg is an abstract base class, defining the common interface of all inference algorithms in libDAI |
  dai::DAIAlg< GRM > | Combines the abstract base class InfAlg with a graphical model (e.g., a FactorGraph or RegionGraph) |
   dai::BP | Approximate inference algorithm "(Loopy) Belief Propagation" |
    dai::FBP | Approximate inference algorithm "Fractional Belief Propagation" [WiH03] |
    dai::TRWBP | Approximate inference algorithm "Tree-Reweighted Belief Propagation" [WJW03] |
   dai::CBP | Class for CBP (Conditioned Belief Propagation) [EaG09] |
   dai::DecMAP | Approximate inference algorithm DecMAP, which constructs a MAP state by decimation |
   dai::ExactInf | Exact inference algorithm using brute force enumeration (mainly useful for testing purposes) |
   dai::Gibbs | Approximate inference algorithm "Gibbs sampling" |
   dai::HAK | Approximate inference algorithm: implementation of single-loop ("Generalized Belief Propagation") and double-loop algorithms by Heskes, Albers and Kappen [HAK03] |
   dai::JTree | Exact inference algorithm using junction tree |
    dai::TreeEP | Approximate inference algorithm "Tree Expectation Propagation" [MiQ04] |
   dai::LC | Approximate inference algorithm "Loop Corrected Belief Propagation" [MoK07] |
   dai::MF | Approximate inference algorithm "Mean Field" |
   dai::MR | Approximate inference algorithm by Montanari and Rizzo [MoR05] |
 dai::BipartiteGraph::levelType | Used internally by isTree() |
 dai::MaximizationStep | A MaximizationStep groups together several parameter estimation tasks (SharedParameters objects) into a single unit |
 dai::BP_dual::messages | Groups together the data structures for storing the two types of messages and their normalizers |
 dai::multifor | Multifor makes it easy to perform a dynamic number of nested for loops |
 dai::Neighbor | Describes the neighbor relationship of two nodes in a graph |
 dai::ParameterEstimation | Base class for parameter estimation methods |
  dai::CondProbEstimation | Estimates the parameters of a conditional probability table, using pseudocounts |
 dai::Permute | Tool for calculating permutations of linear indices of multi-dimensional arrays |
 dai::CBP::Properties | Parameters for CBP |
 dai::BBP::Properties | Parameters for BBP |
 dai::BP::Properties | Parameters for BP |
 dai::DecMAP::Properties | Parameters for DecMAP |
 dai::ExactInf::Properties | Parameters for ExactInf |
 dai::JTree::Properties | Parameters for JTree |
 dai::Gibbs::Properties | Parameters for Gibbs |
 dai::HAK::Properties | Parameters for HAK |
 dai::LC::Properties | Parameters for LC |
 dai::MF::Properties | Parameters for MF |
 dai::TreeEP::Properties | Parameters for TreeEP |
 dai::MR::Properties | Parameters for MR |
 dai::PropertySet | Represents a set of properties, mapping keys (of type PropertyKey) to values (of type PropertyValue) |
 dai::RootedTree | Represents a rooted tree, implemented as a vector of directed edges |
 dai::sequentialVariableElimination | Helper object for dai::ClusterGraph::VarElim() |
 dai::SharedParameters | Represents 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::SmallSet< Var > | |
  dai::VarSet | Represents a set of variables |
   dai::Region | A Region is a set of variables with a counting number |
 dai::State | Makes it easy to iterate over all possible joint states of variables within a VarSet |
 dai::TFactor< T > | Represents a (probability) factor |
  dai::FRegion | An FRegion is a factor with a counting number |
 dai::TFactorSp< T, spvector_type > | Represents a (probability) factor |
 dai::TProb< T > | Represents a vector with entries of type T |
 dai::TProbSp< T, spvector_type > | Represents a vector with entries of type T |
 dai::TreeEP::TreeEPSubTree | Stores the data structures needed to efficiently update the approximation of an off-tree factor |
 dai::UEdge | Represents an undirected edge |
 dai::Var | Represents a discrete random variable |
 dai::WeightedGraph< T > | Represents an undirected weighted graph, with weights of type T, implemented as a std::map mapping undirected edges to weights |