文件名称:BNL
介绍说明－－下载内容均来自于网络，请自行研究使用
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.
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.
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(系统自动生成,下载前可以参看下载内容)
下载文件列表
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
..........\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