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Hidden Markov Model (HMM) Toolbox for Matlab
The new version computes the summed two-slice marginals, to save space (suggestion of Herbert Jaeger).
This toolbox supports inference and learning for HMMs with discrete outputs (dhmm's), Gaussian outputs (ghmm's), or mixtures of Gaussians output (mhmm's). The Gaussians can be full, diagonal, or spherical (isotropic). It also supports discrete inputs, as in a POMDP. The inference routines support filtering, smoothing, and fixed-lag smoothing. For more general models, please see my Bayes Net Toolbox.
Written by Steinar Thorvaldsen, 2004. Last updated: Jan. 2006.
Dept. of Mathematics and Statistics
University of Tromsø - Norway.
MendelHMM is a Hidden Markov Model (HMM) tutorial toolbox for Matlab. These pages describe the graphical user interface (GUI) and the main operations of the program. The toolbox is free for academic use. Ref.: S. Thorvaldsen: A tutorial on Markov models based on Mendel's classical experiments. Journal of Bioinformatics and Computational Biology, Vol. 3, No. 6 (2005), 1441-1460.
To run the program you should make the following steps:
- Download the program to your local PC and install (unzip) it in a directory like …\mendel
Run Matlab v. 6.5 or later
3. Set working directory to ...\mendel (root directory of the toolbox)
4. Type "mendelHMM"
مقاله انگلیسی HHM