Kalman filter algorithm with learning function and demonstration program

filter-Kalman\AR_to_SS.m
.............\convert_to_lagged_form.m
.............\ensure_AR.m
.............\eval_AR_perf.m
.............\kalman_filter.m
.............\kalman_forward_backward.m
.............\kalman_smoother.m
.............\kalman_update.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
.............\........\CVS\Entries
.............\........\...\Entries.Extra
.............\........\...\Entries.Extra.Old
.............\........\...\Entries.Old
.............\........\...\Repository
.............\........\...\Root
.............\........\...\Template
.............\........\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
.............\........\multipdf.m
.............\........\multirnd.m
.............\........\normal_coef.m
.............\........\partial_corr_coef.m
.............\........\parzen.m
.............\........\parzenC.c
.............\........\parzenC.dll
.............\........\parzenC.mexglx
.............\........\parzenC_test.m
.............\........\parzen_fit_select_unif.m
.............\........\pca.m
.............\........\README.txt
.............\........\rndcheck.m
.............\........\sample.m
.............\........\sample_discrete.m
.............\........\sample_gaussian.m
.............\........\standardize.m
.............\........\standardize.m~
.............\........\student_t_logprob.m
.............\........\student_t_prob.m
.............\........\test_dir.m
.............\........\unidrndKPM.m
.............\........\unidrndKPM.m~
.............\........\unif_discrete_sample.m
.............\........\weightedRegression.m
.............\...tools\approxeq.m
.............\........\approx_unique.m

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