Description: 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.
- [SolutionPR] - pattern recognition _ Bayesian classifie
- [MLRMATLAB] - Multiple Linear Regression : MATLAB sour
- [jBNC_src_v.1.2.2] - bayes network toolbox Bayesian classifie
- [BNT] - Bayesian network toolbox.
- [svmclass1] - For support vector regression analysis o
- [Logistic] - logistic regression algorithm code, and
- [GPB] - Generalized pseudo-Bayesian algorithm, t
- [libsvm-2.89] - Main features of LIBLINEAR include Same
File list (Check if you may need any files):
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
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