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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 Bayes Net Toolbox. How to use the HMM toolboxOther packages for HMMs
Recommended reading on HMM
(From:http://www.cs.ubc.ca/~murphyk/Software/HMM/hmm.html) ------------------------------------------------ HMM_隐马尔可夫过程的介绍,希望对初学者有用-HMM_ Hidden Markov process, the hope that useful for beginners
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2023-10-27
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