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