隐马尔科夫链的原理HMMall

  详细介绍了隐马尔科夫链的原理和matlab代码实现，可以运行其中的demo了解hmm的工作原理(Detailed information on hidden Markov chain theory and the matlab code, you can run the demo to understand the working principle hmm)

HMMall
......\HMM
......\...\#fwdback.m#
......\...\#mhmm_em.m#
......\...\#README.txt#
......\...\dhmm_em.m
......\...\dhmm_em_demo.m
......\...\dhmm_em_online.m
......\...\dhmm_em_online_demo.m
......\...\dhmm_logprob.m
......\...\dhmm_logprob_brute_force.m
......\...\dhmm_logprob_path.m
......\...\dhmm_sample.m
......\...\dhmm_sample_endstate.m
......\...\fixed_lag_smoother.m
......\...\fixed_lag_smoother_demo.m
......\...\fwdback.m
......\...\fwdback.m~
......\...\fwdback_xi.m
......\...\fwdprop_backsample.m
......\...\fwdprop_backsample.m~
......\...\gausshmm_train_observed.m
......\...\herbert.txt~
......\...\mc_sample.m
......\...\mc_sample_endstate.m
......\...\mdp_sample.m
......\...\mhmmParzen_train_observed.m
......\...\mhmm_em.m
......\...\mhmm_em.m~
......\...\mhmm_em_demo.m
......\...\mhmm_logprob.m
......\...\mhmm_sample.m
......\...\mk_leftright_transmat.m
......\...\mk_rightleft_transmat.m
......\...\pomdp_sample.m
......\...\publishHMM.m
......\...\README.txt
......\...\README.txt~
......\...\testHMM.m
......\...\transmat_train_observed.m
......\...\viterbi_path.m
......\KPMstats
......\........\#histCmpChi2.m#
......\........\beta_sample.m
......\........\chisquared_histo.m
......\........\chisquared_prob.m
......\........\chisquared_readme.txt
......\........\chisquared_table.m
......\........\clg_Mstep.m
......\........\clg_Mstep_simple.m
......\........\clg_prob.m
......\........\condGaussToJoint.m
......\........\condgaussTrainObserved.m
......\........\condgauss_sample.m
......\........\cond_indep_fisher_z.m
......\........\convertBinaryLabels.m
......\........\cwr_demo.m
......\........\cwr_em.m
......\........\cwr_predict.m
......\........\cwr_prob.m
......\........\cwr_readme.txt
......\........\cwr_test.m
......\........\dirichletpdf.m
......\........\dirichletrnd.m
......\........\dirichlet_sample.m
......\........\distchck.m
......\........\eigdec.m
......\........\est_transmat.m
......\........\fit_paritioned_model_testfn.m
......\........\fit_partitioned_model.m
......\........\gamma_sample.m
......\........\gaussian_prob.m
......\........\gaussian_sample.m
......\........\histCmpChi2.m
......\........\histCmpChi2.m~
......\........\KLgauss.m
......\........\linear_regression.m
......\........\logist2.m
......\........\logist2Apply.m
......\........\logist2ApplyRegularized.m
......\........\logist2Fit.m
......\........\logist2FitRegularized.m
......\........\logistK.m
......\........\logistK_eval.m
......\........\marginalize_gaussian.m
......\........\matrix_normal_pdf.m
......\........\matrix_T_pdf.m
......\........\mc_stat_distrib.m
......\........\mixgauss_classifier_apply.m
......\........\mixgauss_classifier_train.m
......\........\mixgauss_em.m
......\........\mixgauss_init.m
......\........\mixgauss_Mstep.m
......\........\mixgauss_prob.m
......\........\mixgauss_prob_test.m
......\........\mixgauss_sample.m
......\........\mkPolyFvec.m
......\........\mk_unit_norm.m
......\........\multinomial_prob.m
......\........\multinomial_sample.m