libDAI
File List
Here is a list of all documented files with brief descriptions:
[detail level 1234]
  include
  dai
  matlab
 matlab.hDefines some utility functions for interfacing with MatLab
 alldai.hMain libDAI header file. It #includes all other libDAI headers
 bbp.hDefines class BBP, which implements Back-Belief-Propagation
 bipgraph.hDefines the BipartiteGraph class, which represents a bipartite graph
 bp.hDefines class BP, which implements (Loopy) Belief Propagation
 bp_dual.hDefines class BP_dual, which is used primarily by BBP
 cbp.hDefines class CBP, which implements Conditioned Belief Propagation
 clustergraph.hDefines class ClusterGraph, which is used by JTree, TreeEP and HAK
 cobwebgraph.hDefines class CobwebGraph, which implements a type of region graph used by GLC
 createfg.hProvides functionality for input/output of data structures in various file formats
 dag.hDefines the DAG class, which represents a directed acyclic graph
 dai_config.hAllows the user to specify which algorithms will be built into libDAI
 daialg.hDefines the general interface for inference methods in libDAI (classes InfAlg, DaiAlg<>, DaiAlgFG and DaiAlgRG)
 decmap.hDefines class DecMAP, which constructs a MAP state by decimation
 doc.hContains additional doxygen documentation
 emalg.hDefines classes related to Expectation Maximization (EMAlg, ParameterEstimation, CondProbEstimation and SharedParameters)
 enum.hDefines the DAI_ENUM macro, which can be used to define an enum with additional functionality
 evidence.hDefines class Evidence, which stores multiple observations of joint states of variables
 exactinf.hDefines ExactInf class, which can be used for exact inference on small factor graphs
 exceptions.hDefines the Exception class and macros for throwing exceptions and doing assertions
 factor.hDefines TFactor<> and Factor classes which represent factors in probability distributions
 factorgraph.hDefines the FactorGraph class, which represents factor graphs (e.g., Bayesian networks or Markov random fields)
 fbp.hDefines class FBP, which implements Fractional Belief Propagation
 gibbs.hDefines class Gibbs, which implements Gibbs sampling
 glc.hDefines classes GLC and Cobweb, which implement the "Generalized Loop Correction method"
 graph.hDefines the GraphAL class, which represents an undirected graph as an adjacency list
 hak.hDefines class HAK, which implements a variant of Generalized Belief Propagation
 index.hDefines the IndexFor, multifor, Permute and State classes, which all deal with indexing multi-dimensional arrays
 io.hProvides functionality for input/output of data structures in various file formats
 jtree.hDefines class JTree, which implements the junction tree algorithm
 lc.hDefines class LC, which implements loop corrections for approximate inference
 mf.hDefines class MF which implements the Mean Field algorithm
 mr.hDefines class MR, which implements loop corrections as proposed by Montanari and Rizzo
 prob.hDefines TProb<> and Prob classes which represent (probability) vectors (e.g., probability distributions of discrete random variables)
 properties.hDefines the Property and PropertySet classes, which are mainly used for managing parameters of inference algorithms
 regiongraph.hDefines classes Region, FRegion and RegionGraph, which implement a particular subclass of region graphs
 smallset.hDefines the SmallSet<> class, which represents a set (optimized for a small number of elements)
 treeep.hDefines class TreeEP, which implements Tree Expectation Propagation
 trwbp.hDefines class TRWBP, which implements Tree-Reweighted Belief Propagation
 util.hDefines general utility functions and adds an abstraction layer for platform-dependent functionality
 var.hDefines class Var, which represents a discrete random variable
 varset.hDefines the VarSet class, which represents a set of random variables
 weightedgraph.hDefines some utility functions for (weighted) undirected graphs, trees and rooted trees
 config.h