文件列表:

viterbi_path.m (1646, 2003-07-29)
em_converged.m (1205, 2003-06-28)
fwdback.m (5975, 2003-07-02)
gen_HMM_em.m (4012, 2004-01-16)
HMM_logprob.m (691, 2003-06-30)
init_diagonal.m (3618, 2004-05-03)
init_grid.m (1715, 2004-01-23)
init_random.m (1390, 2004-05-04)
mendelHMM.fig (48064, 2004-01-16)
mendelHMM.m (18892, 2004-05-04)
mk_stochastic.m (835, 2003-06-28)
multinomial_prob.m (591, 2003-06-28)
normalise.m (708, 2003-06-28)
process_options.m (4526, 2002-12-29)
resample_mendeldata.m (2463, 2004-01-23)
sample_discr.m (979, 2004-01-23)
sample_HMM.m (1658, 2004-01-23)
sample_MC.m (669, 2004-01-23)
set_diploidmode.m (1114, 2004-01-23)
set_tetraploidmode.m (1496, 2004-01-23)
view_dataseq.m (1731, 2004-01-23)
view_mendeldata.m (4257, 2004-05-03)
view_sampleddata.m (1288, 2004-05-04)

Mendel HMM Toolbox for Matlab. By Steinar Thorvaldsen, Dept of Mathematics and Statistics University of Troms - Norway. Version 1.0. January 2004. steinart@math.uit.no http://www.math.uit.no/bi/hmm/ MendelHMM is a Hidden Markov Model (HMM) tutorial toolbox for Matlab. The toolbox is free for academic use. To run the program you should make following steps: 1. Download the program to your local PC and install it in a directory like …\mendel 2. Run Matlab v. 6.0 or later 3. Set working directory to ...\mendel (root directory of the toolbox) 4. Type "mendelHMM" When you type "mendelHMM" in Matlab command window the main window of GUI will appear. In left part of main window there is a frame containing controls for loading and managing training data. On right there is a frame containing controls for managing the Hidden Markov model. Button " >Estimate (EM)>" runs estimation of model from the selected training set. Baum Welch algorithm is used starting from the pre-selected number of states and the initialization method, but any other values of hidden parameters are ignored. Button "

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