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CVS
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README.txt
README.txt
 
 
dhmm_em.m
dhmm_em.m
 
 
dhmm_em_demo.m
dhmm_em_demo.m
 
 
dhmm_em_online.m
dhmm_em_online.m
 
 
dhmm_em_online_demo.m
dhmm_em_online_demo.m
 
 
dhmm_logprob.m
dhmm_logprob.m
 
 
dhmm_logprob_brute_force.m
dhmm_logprob_brute_force.m
 
 
dhmm_logprob_path.m
dhmm_logprob_path.m
 
 
dhmm_sample.m
dhmm_sample.m
 
 
dhmm_sample_endstate.m
dhmm_sample_endstate.m
 
 
fixed_lag_smoother.m
fixed_lag_smoother.m
 
 
fixed_lag_smoother_demo.m
fixed_lag_smoother_demo.m
 
 
fwdback.m
fwdback.m
 
 
gausshmm_train_observed.m
gausshmm_train_observed.m
 
 
mc_sample.m
mc_sample.m
 
 
mc_sample_endstate.m
mc_sample_endstate.m
 
 
mdp_sample.m
mdp_sample.m
 
 
mhmmParzen_train_observed.m
mhmmParzen_train_observed.m
 
 
mhmm_em.m
mhmm_em.m
 
 
mhmm_em_demo.m
mhmm_em_demo.m
 
 
mhmm_logprob.m
mhmm_logprob.m
 
 
mhmm_sample.m
mhmm_sample.m
 
 
mk_leftright_transmat.m
mk_leftright_transmat.m
 
 
mk_rightleft_transmat.m
mk_rightleft_transmat.m
 
 
pomdp_sample.m
pomdp_sample.m
 
 
testHMM.m
testHMM.m
 
 
transmat_train_observed.m
transmat_train_observed.m
 
 
viterbi_path.m
viterbi_path.m
 
 

README.txt

Hidden Markov Model (HMM) Toolbox written by Kevin Murphy (1998).
See http://www.ai.mit.edu/~murphyk/Software/hmm.html for details.

Models
------

dhmm = HMM with discrete output
mhmm = HMM with mixture of Gaussians output;
 Use mhmm with M=1 components to simulate an HMM with a single Gaussian output.

Demos
-----

mhmm_em_demo
dhmm_em_demo
dhmm_em_online_demo
fixed_lag_smoother_demo

References
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See "A tutorial on Hidden Markov Models and selected applications in speech recognition",
 L. Rabiner, 1989, Proc. IEEE 77(2):257--286.