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