To classify a sequence into one of k classes, train up k HMMs, one per class, and then compute the log-likelihood that each model gives to the test sequence if the i'th model is the most likely, then declare the class of the sequence to be class i. Sequence classificationTo evaluate the log-likelihood of a trained model given test data, proceed as follows: loglik = dhmm_logprob(data, prior, transmat, obsmat)Note: the discrete alphabet is assumed to be, where O = size(obsmat, 2). = dhmm_em(data, prior1, transmat1, obsmat1, 'max_iter', 5) LL(t) is the log-likelihood after iteration t, so we can plot the learning curve. T=10 nex=20 data = dhmm_sample(prior0, transmat0, obsmat0, nex, T) Now we make a random guess as to what the parameters are, prior1 = normalise(rand(Q,1)) transmat1 = mk_stochastic(rand(Q,Q)) obsmat1 = mk_stochastic(rand(Q,O)) and improve our guess using 5 iterations of EM. O = 3 Q = 2 prior0 = normalise(rand(Q,1)) transmat0 = mk_stochastic(rand(Q,Q)) obsmat0 = mk_stochastic(rand(Q,O)) Now we sample nex=20 sequences of length T=10 each from this model, to use as training data. We create random stochastic matrices as follows. Let there be Q=2 states and O=2 output symbols. How to use the HMM toolboxHMMs with discrete outputsMaximum likelihood parameter estimation using EM (Baum Welch)The script dhmm_em_demo.m gives an example of how to learn an HMM with discrete outputs. Please see recommended reading for details. For example, the hidden states may represent words or phonemes, and the observations represent the acoustic signal.

You only see the observations, and the goal is to infer the hidden state sequence.

What is an HMM?An HMM is a Markov chain, where each state generates an observation. For more general models, please see my Bayes Net Toolbox. The inference routines support filtering, smoothing, and fixed-lag smoothing. It also supports discrete inputs, as in a POMDP. The Gaussians can be full, diagonal, or spherical (isotropic). 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 interface has changed! This documentation refers to the version of or later. Hidden Markov Model (HMM) Toolbox for MatlabWritten by Kevin Murphy, 1998.

Hidden markov model matlab