BNL

  The BNL toolbox is a set of Matlab functions for defining and estimating the parameters of a Bayesian network for discrete variables in which the conditional probability tables are specified by logistic regression models. Logistic regression can be used to incorporate restrictions on the conditional probabilities and to account for the effect of covariates. Nominal variables are modeled with multinomial logistic regression, whereas the category probabilities of ordered variables are modeled through a cumulative or adjacent-categories response function. Variables can be observed, partially observed, or hidden.

additional
..........\adj_logistic.m
..........\adj_logit.m
..........\cum_logistic.m
..........\cum_logit.m
..........\deriv_adj_logist.m
..........\deriv_cum_logist.m
..........\deriv_multinom_logist.m
..........\dummycode.m
..........\infocrit.m
..........\multinom_logistic.m
..........\multinom_logit.m
..........\multinornd.m
..........\randvector.m
BNL manual.pdf
constructbnt
............\franks_from_BNT.m
............\franks_mk_adj_mat.m
............\inv_order.m
............\link_pot_to_CPT.m
designmatrices
..............\check_order.m
..............\construct_design_mats.m
..............\construct_lin_pred.m
..............\construct_predmat.m
..............\cov_into_design.m
..............\define_lin_pred_struct_cov_default.m
..............\define_lin_pred_struct_cov_main.m
..............\define_lin_pred_struct_main.asv
..............\define_lin_pred_struct_main.m
..............\define_lin_pred_struct_sat.m
estimation
..........\compute_JPTs.m
..........\compute_suff_stats.m
..........\compute_suff_stats_ind.m
..........\construct_bigCPTs.m
..........\construct_equiv_class_CPT.m
..........\construct_sCPT.m
..........\EM_iteration.m
..........\find_max_configs.asv
..........\find_max_configs.m
..........\fit_multinom_logistic.m
..........\fit_ordered_logistic.m
..........\gen_random_start.m
..........\loglik.m
..........\max_marginalization.m
..........\num_infomatrix_anal_score.m
..........\score.m
..........\update_parms.m
example_models
..............\alarm with restrictions
..............\.......................\comparemodels.m
..............\.......................\construct_alarm.m
..............\.......................\fit_model_cumul.m
..............\.......................\fit_model_cumul50.asv
..............\.......................\fit_model_cumul50.m
..............\.......................\fit_model_cumul50_test.asv
..............\.......................\fit_model_cumul50_test.m
..............\.......................\fit_model_cumul_test.m
..............\.......................\fit_model_norest.asv
..............\.......................\fit_model_norest.m
..............\.......................\fit_model_norest_test.asv
..............\.......................\fit_model_norest_test.m
..............\.......................\gen_alarm_start.asv
..............\.......................\gen_alarm_start.m
..............\.......................\simulate50_50.m
..............\anorex
..............\......\construct_bnet_hier_hmm.m
..............\......\construct_bnet_hmm.m
..............\......\construct_equiv_hier_hmm.m
..............\......\define_lin_pred_struct_hier_hmm_main.m
..............\......\equiv_classes_hier_hmm.m
..............\......\equiv_classes_hmm.m
..............\......\fit_model_hier_hmm.m
..............\......\fit_model_hier_hmm_maineffects.m
..............\......\fit_model_hier_hmm_time.m
..............\......\fit_model_hier_hmm_timesq.m
..............\......\fit_model_hmm.m
..............\......\link_covariates_to_nodes_hier_hmm_time.m
..............\......\link_covariates_to_nodes_hier_hmm_timesq.m
..............\......\loadtime.m
..............\......\loadtimesamplingdata.m
..............\brain
..............\.....\construct_bnet_hmm_theta.m
..............\.....\fit_modelbrain_domain_theta.m
..............\.....\fit_modelbrain_domain_theta_treat.m
..............\.....\fit_modelbrain_hmm.m
..............\.....\fit_modelbrain_hmm_domain.m
..............\hmm
..............\...\construct_bnet_hmm.m
..............\...\fit_model_hmm.m
..............\...\generate_hmm_data.m
..............\...\hmm.xls
..............\mixed_lltm
..............\..........\construct_bnet_mixlltm.m
..............\..........\fit_model_mixed_lltm.m
gausskwad
.........\herzo.m
generate_data
.............\generate_bnet_data.m