| Cdai::BBP | Implements BBP (Back-Belief-Propagation) [EaG09] |
| Cdai::BBPCostFunction | Predefined cost functions that can be used with BBP |
| Cdai::BP_dual::beliefs | Groups together the data structures for storing the two types of beliefs and their normalizers |
| Cdai::BipartiteGraph | Represents the neighborhood structure of nodes in an undirected, bipartite graph |
| Cdai::BP_dual | Calculates both types of BP messages and their normalizers from an InfAlg |
| Cdai::ClusterGraph | A ClusterGraph is a hypergraph with variables as nodes, and "clusters" (sets of variables) as hyperedges |
| Cdai::CobwebGraph::Connection | The information in connection between two regions |
| Cdai::DAG | Represents the neighborhood structure of nodes in a directed cyclic graph |
| Cdai::DEdge | Represents a directed edge |
| Cdai::BP::EdgeProp | Type used for storing edge properties |
| Cdai::EMAlg | EMAlg performs Expectation Maximization to learn factor parameters |
| Cdai::Evidence | Stores a data set consisting of multiple samples, where each sample is the observed joint state of some variables |
| ▼Cstd::exception | STL class |
| ▼Cstd::runtime_error | STL class |
| Cdai::Exception | Error handling in libDAI is done by throwing an instance of the Exception class |
| ▼Cdai::FactorGraph | Represents a factor graph |
| Cdai::CobwebGraph | A CobwebGraph is a special type of region graph used by the GLC algorithm |
| Cdai::RegionGraph | A RegionGraph combines a bipartite graph consisting of outer regions (type FRegion) and inner regions (type Region) with a FactorGraph |
| Cdai::fo_abs< T > | Function object that takes the absolute value |
| Cdai::fo_absdiff< T > | Function object that returns the absolute difference of x and y |
| Cdai::fo_divides0< T > | Function object similar to std::divides(), but different in that dividing by zero results in zero |
| Cdai::fo_exp< T > | Function object that takes the exponent |
| Cdai::fo_Hellinger< T > | Function object useful for calculating the Hellinger distance |
| Cdai::fo_id< T > | Function object that returns the value itself |
| Cdai::fo_inv< T > | Function object that takes the inverse |
| Cdai::fo_inv0< T > | Function object that takes the inverse, except that 1/0 is defined to be 0 |
| Cdai::fo_KL< T > | Function object useful for calculating the KL distance |
| Cdai::fo_log< T > | Function object that takes the logarithm |
| Cdai::fo_log0< T > | Function object that takes the logarithm, except that log(0) is defined to be 0 |
| Cdai::fo_max< T > | Function object that returns the maximum of two values |
| Cdai::fo_min< T > | Function object that returns the minimum of two values |
| Cdai::fo_plog0p< T > | Function object that returns p*log0(p) |
| Cdai::fo_pow< T > | Function object that returns x to the power y |
| Cdai::GraphAL | Represents the neighborhood structure of nodes in an undirected graph |
| Cdai::greedyVariableElimination | Helper object for dai::ClusterGraph::VarElim() |
| Cdai::hash_map< T, U, H > | Hash_map is an alias for std::tr1::unordered_map |
| Cdai::IndexFor | Tool for looping over the states of several variables |
| ▼Cdai::InfAlg | InfAlg is an abstract base class, defining the common interface of all inference algorithms in libDAI |
| ▼Cdai::DAIAlg< GRM > | Combines the abstract base class InfAlg with a graphical model (e.g., a FactorGraph or RegionGraph) |
| ▼Cdai::BP | Approximate inference algorithm "(Loopy) Belief Propagation" |
| Cdai::FBP | Approximate inference algorithm "Fractional Belief Propagation" [WiH03] |
| Cdai::TRWBP | Approximate inference algorithm "Tree-Reweighted Belief Propagation" [WJW03] |
| Cdai::CBP | Class for CBP (Conditioned Belief Propagation) [EaG09] |
| Cdai::ExactInf | Exact inference algorithm using brute force enumeration (mainly useful for testing purposes) |
| Cdai::HAK | Approximate inference algorithm: implementation of single-loop ("Generalized Belief Propagation") and double-loop algorithms by Heskes, Albers and Kappen [HAK03] |
| ▼Cdai::JTree | Exact inference algorithm using junction tree |
| Cdai::TreeEP | Approximate inference algorithm "Tree Expectation Propagation" [MiQ04] |
| Cdai::LC | Approximate inference algorithm "Loop Corrected Belief Propagation" [MoK07] |
| Cdai::MF | Approximate inference algorithm "Mean Field" |
| Cdai::MR | Approximate inference algorithm by Montanari and Rizzo [MoR05] |
| Cdai::BipartiteGraph::levelType | Used internally by isTree() |
| ▼Cstd::map< K, T > | STL class |
| Cdai::PropertySet | Represents a set of properties, mapping keys (of type PropertyKey) to values (of type PropertyValue) |
| Cdai::WeightedGraph< T > | Represents an undirected weighted graph, with weights of type T, implemented as a std::map mapping undirected edges to weights |
| Cdai::MaximizationStep | A MaximizationStep groups together several parameter estimation tasks (SharedParameters objects) into a single unit |
| Cdai::BP_dual::messages | Groups together the data structures for storing the two types of messages and their normalizers |
| Cdai::multifor | Multifor makes it easy to perform a dynamic number of nested for loops |
| Cdai::Neighbor | Describes the neighbor relationship of two nodes in a graph |
| ▼Cdai::ParameterEstimation | Base class for parameter estimation methods |
| Cdai::CondProbEstimation | Estimates the parameters of a conditional probability table, using pseudocounts |
| Cdai::Permute | Tool for calculating permutations of linear indices of multi-dimensional arrays |
| Cdai::MR::Properties | Parameters for MR |
| Cdai::BP::Properties | Parameters for BP |
| Cdai::ExactInf::Properties | Parameters for ExactInf |
| Cdai::HAK::Properties | Parameters for HAK |
| Cdai::JTree::Properties | Parameters for JTree |
| Cdai::MF::Properties | Parameters for MF |
| Cdai::LC::Properties | Parameters for LC |
| Cdai::TreeEP::Properties | Parameters for TreeEP |
| Cdai::BBP::Properties | Parameters for BBP |
| Cdai::CBP::Properties | Parameters for CBP |
| Cdai::sequentialVariableElimination | Helper object for dai::ClusterGraph::VarElim() |
| ▼Cstd::set< K > | STL class |
| Cdai::GraphEL | Represents an undirected graph, implemented as a std::set of undirected edges |
| Cdai::SharedParameters | Represents a single factor or set of factors whose parameters should be estimated |
| Cdai::SmallSet< T > | Represents a set; the implementation is optimized for a small number of elements |
| Cdai::SmallSet< size_t > | |
| ▼Cdai::SmallSet< Var > | |
| ▼Cdai::VarSet | Represents a set of variables |
| Cdai::Region | A Region is a set of variables with a counting number |
| Cdai::State | Makes it easy to iterate over all possible joint states of variables within a VarSet |
| Cdai::TFactor< T > | Represents a (probability) factor |
| ▼Cdai::TFactor< Real > | |
| Cdai::FRegion | An FRegion is a factor with a counting number |
| Cdai::TProb< T > | Represents a vector with entries of type T |
| Cdai::TProb< Real > | |
| Cdai::TreeEP::TreeEPSubTree | Stores the data structures needed to efficiently update the approximation of an off-tree factor |
| Cdai::UEdge | Represents an undirected edge |
| Cdai::Var | Represents a discrete random variable |
| ▼Cstd::vector< T > | STL class |
| Cdai::BP_dual::_edges_t< dai::TProb > | |
| Cdai::BP_dual::_edges_t< Real > | |
| Cdai::BP_dual::_edges_t< T > | Convenience label for storing edge properties |
| Cdai::RootedTree | Represents a rooted tree, implemented as a vector of directed edges |
