aGrUM  0.14.1
Scores and Independence Tests
+ Collaboration diagram for Scores and Independence Tests:

Detailed Description

Classes

class  gum::learning::CorrectedMutualInformation< ALLOC >
 The class computing n times the corrected mutual information, as used in the 3off2 algorithm. More...
 
class  gum::learning::IdSetIterator< ALLOC >
 The iterators for IdSets. More...
 
class  gum::learning::IdSet< ALLOC >
 A class for storing a pair of sets of NodeIds, the second one corresponding to a conditional set. More...
 
class  gum::learning::IndependenceTest< ALLOC >
 The base class for all the independence tests used for learning. More...
 
class  gum::learning::IndepTestChi2< ALLOC >
 the class for computing Chi2 independence test scores More...
 
class  gum::learning::IndepTestG2< ALLOC >
 the class for computing G2 independence test scores More...
 
class  gum::learning::KNML< ALLOC >
 the class for computing the NML penalty used by 3off2 More...
 
class  gum::learning::RecordCounter< ALLOC >
 The class that computes countings of observations from the database. More...
 
class  gum::learning::Score< ALLOC >
 The base class for all the scores used for learning (BIC, BDeu, etc) More...
 
class  gum::learning::ScoreAIC< ALLOC >
 the class for computing AIC scores More...
 
class  gum::learning::ScoreBD< ALLOC >
 the class for computing Bayesian Dirichlet (BD) log2 scores More...
 
class  gum::learning::ScoreBDeu< ALLOC >
 the class for computing BDeu scores More...
 
class  gum::learning::ScoreBIC< ALLOC >
 the class for computing BIC scores More...
 
class  gum::learning::ScorefNML< ALLOC >
 the class for computing fNML scores More...
 
class  gum::learning::ScoreK2< ALLOC >
 the class for computing K2 scores (actually their log2 value) More...
 
class  gum::learning::ScoreLog2Likelihood< ALLOC >
 the class for computing Log2-likelihood scores More...
 
class  ScoreMDL
 the class for computing MDL scores More...
 
class  gum::learning::ScoringCache< ALLOC >
 a cache for caching scores and independence tests resultsCaching previously computed scores or the results of conditional independence tests is very important for learning algorithms because computing a score or an independence test requires parsing the database and this is the most time consuming operation in learning. More...