dai::BBP | Implements BBP (Back-Belief-Propagation) [EaG09] |
dai::BBP::Properties | Parameters for BBP |
dai::BBPCostFunction | Predefined cost functions that can be used with BBP |
dai::BipartiteGraph | Represents the neighborhood structure of nodes in an undirected, bipartite graph |
dai::BipartiteGraph::levelType | Used internally by isTree() |
dai::BP | Approximate inference algorithm "(Loopy) Belief Propagation" |
dai::BP::EdgeProp | Type used for storing edge properties |
dai::BP::Properties | Parameters for BP |
dai::BP_dual | Calculates 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::beliefs | Groups together the data structures for storing the two types of beliefs and their normalizers |
dai::BP_dual::messages | Groups together the data structures for storing the two types of messages and their normalizers |
dai::CBP | Class for CBP (Conditioned Belief Propagation) [EaG09] |
dai::CBP::Properties | Parameters for CBP |
dai::ClusterGraph | A ClusterGraph is a hypergraph with variables as nodes, and "clusters" (sets of variables) as hyperedges |
dai::CondProbEstimation | Estimates the parameters of a conditional probability table, using pseudocounts |
dai::DAG | Represents 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::DecMAP | Approximate inference algorithm DecMAP, which constructs a MAP state by decimation |
dai::DecMAP::Properties | Parameters for DecMAP |
dai::DEdge | Represents a directed edge |
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::ExactInf | Exact inference algorithm using brute force enumeration (mainly useful for testing purposes) |
dai::ExactInf::Properties | Parameters for ExactInf |
dai::Exception | Error handling in libDAI is done by throwing an instance of the Exception class |
dai::FactorGraph | Represents a factor graph |
dai::FBP | Approximate 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::FRegion | An FRegion is a factor with a counting number |
dai::Gibbs | Approximate inference algorithm "Gibbs sampling" |
dai::Gibbs::Properties | Parameters for Gibbs |
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::HAK | Approximate inference algorithm: implementation of single-loop ("Generalized Belief Propagation") and double-loop algorithms by Heskes, Albers and Kappen [HAK03] |
dai::HAK::Properties | Parameters for HAK |
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::JTree | Exact inference algorithm using junction tree |
dai::JTree::Properties | Parameters for JTree |
dai::LC | Approximate inference algorithm "Loop Corrected Belief Propagation" [MoK07] |
dai::LC::Properties | Parameters for LC |
dai::MaximizationStep | A MaximizationStep groups together several parameter estimation tasks (SharedParameters objects) into a single unit |
dai::MF | Approximate inference algorithm "Mean Field" |
dai::MF::Properties | Parameters for MF |
dai::MR | Approximate inference algorithm by Montanari and Rizzo [MoR05] |
dai::MR::Properties | Parameters for MR |
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::Permute | Tool for calculating permutations of linear indices of multi-dimensional arrays |
dai::PropertySet | Represents a set of properties, mapping keys (of type PropertyKey) to values (of type PropertyValue) |
dai::Region | A Region is a set of variables with a counting number |
dai::RegionGraph | A RegionGraph combines a bipartite graph consisting of outer regions (type FRegion) and inner regions (type Region) with a FactorGraph |
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::State | Makes 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::TreeEP | Approximate inference algorithm "Tree Expectation Propagation" [MiQ04] |
dai::TreeEP::Properties | Parameters for TreeEP |
dai::TreeEP::TreeEPSubTree | Stores the data structures needed to efficiently update the approximation of an off-tree factor |
dai::TRWBP | Approximate inference algorithm "Tree-Reweighted Belief Propagation" [WJW03] |
dai::UEdge | Represents an undirected edge |
dai::Var | Represents a discrete random variable |
dai::VarSet | Represents 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 |