| ▼Ndai | Namespace for libDAI |
| ▼CBBP | Implements BBP (Back-Belief-Propagation) [EaG09] |
| CProperties | Parameters for BBP |
| CBBPCostFunction | Predefined cost functions that can be used with BBP |
| ▼CBipartiteGraph | Represents the neighborhood structure of nodes in an undirected, bipartite graph |
| ClevelType | Used internally by isTree() |
| ▼CBP | Approximate inference algorithm "(Loopy) Belief Propagation" |
| CEdgeProp | Type used for storing edge properties |
| CProperties | Parameters for BP |
| ▼CBP_dual | Calculates both types of BP messages and their normalizers from an InfAlg |
| C_edges_t | Convenience label for storing edge properties |
| Cbeliefs | Groups together the data structures for storing the two types of beliefs and their normalizers |
| Cmessages | Groups together the data structures for storing the two types of messages and their normalizers |
| ▼CCBP | Class for CBP (Conditioned Belief Propagation) [EaG09] |
| CProperties | Parameters for CBP |
| CClusterGraph | A ClusterGraph is a hypergraph with variables as nodes, and "clusters" (sets of variables) as hyperedges |
| ▼CCobwebGraph | A CobwebGraph is a special type of region graph used by the GLC algorithm |
| CConnection | The information in connection between two regions |
| CCondProbEstimation | Estimates the parameters of a conditional probability table, using pseudocounts |
| CDAG | Represents the neighborhood structure of nodes in a directed cyclic graph |
| CDAIAlg | Combines the abstract base class InfAlg with a graphical model (e.g., a FactorGraph or RegionGraph) |
| CDEdge | Represents a directed edge |
| CEMAlg | EMAlg performs Expectation Maximization to learn factor parameters |
| CEvidence | Stores a data set consisting of multiple samples, where each sample is the observed joint state of some variables |
| ▼CExactInf | Exact inference algorithm using brute force enumeration (mainly useful for testing purposes) |
| CProperties | Parameters for ExactInf |
| CException | Error handling in libDAI is done by throwing an instance of the Exception class |
| CFactorGraph | Represents a factor graph |
| CFBP | Approximate inference algorithm "Fractional Belief Propagation" [WiH03] |
| Cfo_abs | Function object that takes the absolute value |
| Cfo_absdiff | Function object that returns the absolute difference of x and y |
| Cfo_divides0 | Function object similar to std::divides(), but different in that dividing by zero results in zero |
| Cfo_exp | Function object that takes the exponent |
| Cfo_Hellinger | Function object useful for calculating the Hellinger distance |
| Cfo_id | Function object that returns the value itself |
| Cfo_inv | Function object that takes the inverse |
| Cfo_inv0 | Function object that takes the inverse, except that 1/0 is defined to be 0 |
| Cfo_KL | Function object useful for calculating the KL distance |
| Cfo_log | Function object that takes the logarithm |
| Cfo_log0 | Function object that takes the logarithm, except that log(0) is defined to be 0 |
| Cfo_max | Function object that returns the maximum of two values |
| Cfo_min | Function object that returns the minimum of two values |
| Cfo_plog0p | Function object that returns p*log0(p) |
| Cfo_pow | Function object that returns x to the power y |
| CFRegion | An FRegion is a factor with a counting number |
| CGraphAL | Represents the neighborhood structure of nodes in an undirected graph |
| CGraphEL | Represents an undirected graph, implemented as a std::set of undirected edges |
| CgreedyVariableElimination | Helper object for dai::ClusterGraph::VarElim() |
| ▼CHAK | Approximate inference algorithm: implementation of single-loop ("Generalized Belief Propagation") and double-loop algorithms by Heskes, Albers and Kappen [HAK03] |
| CProperties | Parameters for HAK |
| Chash_map | Hash_map is an alias for std::tr1::unordered_map |
| CIndexFor | Tool for looping over the states of several variables |
| CInfAlg | InfAlg is an abstract base class, defining the common interface of all inference algorithms in libDAI |
| ▼CJTree | Exact inference algorithm using junction tree |
| CProperties | Parameters for JTree |
| ▼CLC | Approximate inference algorithm "Loop Corrected Belief Propagation" [MoK07] |
| CProperties | Parameters for LC |
| CMaximizationStep | A MaximizationStep groups together several parameter estimation tasks (SharedParameters objects) into a single unit |
| ▼CMF | Approximate inference algorithm "Mean Field" |
| CProperties | Parameters for MF |
| ▼CMR | Approximate inference algorithm by Montanari and Rizzo [MoR05] |
| CProperties | Parameters for MR |
| Cmultifor | Multifor makes it easy to perform a dynamic number of nested for loops |
| CNeighbor | Describes the neighbor relationship of two nodes in a graph |
| CParameterEstimation | Base class for parameter estimation methods |
| CPermute | Tool for calculating permutations of linear indices of multi-dimensional arrays |
| CPropertySet | Represents a set of properties, mapping keys (of type PropertyKey) to values (of type PropertyValue) |
| CRegion | A Region is a set of variables with a counting number |
| CRegionGraph | A RegionGraph combines a bipartite graph consisting of outer regions (type FRegion) and inner regions (type Region) with a FactorGraph |
| CRootedTree | Represents a rooted tree, implemented as a vector of directed edges |
| CsequentialVariableElimination | Helper object for dai::ClusterGraph::VarElim() |
| CSharedParameters | Represents a single factor or set of factors whose parameters should be estimated |
| CSmallSet | Represents a set; the implementation is optimized for a small number of elements |
| CState | Makes it easy to iterate over all possible joint states of variables within a VarSet |
| CTFactor | Represents a (probability) factor |
| CTProb | Represents a vector with entries of type T |
| ▼CTreeEP | Approximate inference algorithm "Tree Expectation Propagation" [MiQ04] |
| CProperties | Parameters for TreeEP |
| CTreeEPSubTree | Stores the data structures needed to efficiently update the approximation of an off-tree factor |
| CTRWBP | Approximate inference algorithm "Tree-Reweighted Belief Propagation" [WJW03] |
| CUEdge | Represents an undirected edge |
| CVar | Represents a discrete random variable |
| CVarSet | Represents a set of variables |
| CWeightedGraph | Represents an undirected weighted graph, with weights of type T, implemented as a std::map mapping undirected edges to weights |
