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% BNT Structure Learning Package
% http://banquiseasi.insa-rouen.fr/projects/bnt-slp/
%
% Some usefull add-ons for Bayes Net Toolbox
% ==================================================================================================
% v1.5 : 20 avril 2008
% ==================================================================================================
%
% Data manipulation
% -----------------
%
% - mat_to_bnt : matrix to cell array conversion (with missing data encoding in the matrix)
% - hist_ic : optimal Histogram based on IC information criterion (PhL)
% - histc_ic : Histogram count , for HIST_IC edges (PhL)
%
%
% Bayes Net Structure Learning
% ----------------------------
%
% - cond_indep_chisquare : test if X indep Y given Z using ChiSquare test (Pearson's or Likelihood Ratio Test) (PhL,OF)
% (for use with LEARN_STRUCT_PDAG_PC or LEARN_STRUCT_PDAG_IC_STAR)
% - test_chisquare : test for COND_INDEP_CHISQUARE function
% - test_pc : test for LEARN_STRUCT_PDAG_PC function with ChiSquare test (PhL)
%
% - mk_alarm_bnet : make the bnet of ALARM network (WH) (used in test_sem3)
% - mk_asia_bnet : make the bnet of ASIA network (PhL,OF) (used in test functions)
%
% - mk_naive_struct : generate the naive bayes structure (for a given class node)
%
% - learn_struct_mwst : structure learning using maximum spanning tree (OF, PhL)
% - mutual_info_score : mutual information scoring (OF, PhL, WXY)
% - test_mwst : test for LEARN_STRUCT_MWST function (PhL)
%
% - learn_struct_tan : structure learning giving the best tree augmented naive bayes classifier (OF, PHL, NS)
%
% - learn_struct_hc : structure learning using hill climbing (GL)
% - learn_struct_gs : structure learning using greedy search (GL)
% - learn_struct_gs2 : structure learning using greedy search with cache implementation (GL, WH, OF, PhL)
% (use mk_nbrs_of_dag_topo (developped WH) instead of mk_nbrs_of_dag)
% - score_init_cache : cache initialisation for local score computation (OF, PhL)
% - score_family : new version of BNT function with cache implementation (OF, PhL)
% - score_dags : new version of BNT function with cache implementation (OF, PhL, DH)
% - test_gs2 : test for LEARN_STRUCT_GS2 function (PhL)
%
% - learn_struct_ges : structure learning using Greedy Equivalence Search (PhL)
% - mk_nbrs_of_pdag_add : generate the sup. inclusion boundary of a given pdag (PhL)
% - mk_nbrs_of_pdag_del : generate the inf. inclusion boundary of a given pdag (PhL)
% - test_ges : test for LEARN_STRUCT_GES function (PhL)
%
% - learn_struct_EM : structure learning using structural EM (WH)
% - multiply_one_marginal.c
% - mk_nbrs_of_dag_topo : generate the neighbours of a given dag (WH)
% (better implementation than mk_nbrs_of_dag)
% - test_sem1 : demo 1 (SPRINKER)
% - test_sem2 : demo 2 (DISCRETE1)
% - test_sem3 : demo 3 (ALARM)
%
% - learn_struct_mwst_EM : MWST structure learning with missing data (OF, PhL)
%
% - cpdag_to_dag : return a dag for a given CPDAG (OF, PhL)
% - dag_to_cpdag : return the CPDAG, representant of the equivalent class of the dag (OF, PhL)
% - pdag_to_dag : return a DAG that instantiates the given pdag [Dor&Tarsi] (PhL, OF)
% - test_cpdag : test CPDAG_to_DAG and DAG_to_CPDAG functions (PhL)
% - kl_divergence : Kullback-Leibler divergence between two bnet distributions (PhL)
% - kl_divergence2 : Kullback-Leibler divergence between two bnet distributions (PhL)
%
% - test_structure : runs all the test functions (PhL)
%
% - gener_MCAR_net : to gener a BN which modelise a process of incomplete data generation with MCAR assumptions (OF)
% - gener_MAR_net : to gener a BN which modelise a process of incomplete data generation with MAR assumptions (OF)
% - gener_data_from_bnet_miss: to gener a incomplete dataset from a BN creted with gener_MCAR_net or gener_MAR_net (OF)
%
% - editing_dist : editing distance between two DAG (PhL)
% (memory optimization, but quite slow !)
% - learn_struct_bnpc : structure learning with BN-Power constructor (OF, PhL)
% - learn_struct_tan_EM : Tree Augmented Naive Bayes structure learning with missing data (OF, PhL)
% - learn_struct_ges_EM : structure learning in Markov equivalent space with missing data (OF, HB, PhL)
% - ......
%
%
% Contributors :
% ------------
% LITIS Rouen, France
% - PhL : Philippe Leray (philippe.leray@univ-nantes.fr)
% - OF : Olivier Francois (francois.olivier.c.h@gmail.com)
%
% External parts
% - GL : Gang Li, Deakin University (gangli@deakin.edu.au)
% - WH : Wei Hu, Intel (wei.hu@intel.com)
% - DH : Derek Hoiem (dhoiem@cs.cmu.edu)
% - NS : Navid Serrano, Jet Propulsion Laboratory (Navid.Serrano@jpl.nasa.gov)
% - WXY : Wang Xiang Yang, Shanghai JiaoTong University (wangxiangyang@sjtu.edu.cn)
% - HB : Hanene Borchani (hanene.borchani@gmail.com)
%
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