hmm-learn {stochmod} | R Documentation |
Maximum Likelihood learning via EM
HMM.learn( xL, K, vL=NULL, hmm.init=NULL, tol=1e-03, LLstop=Inf, min.iter=3, max.iter=Inf )
xL |
Either a matrix or a list of matrices containing training observation sequences, with one sample per row |
K |
Desired number of states |
vL |
Either a matrix or a list of matrices containing validation observation sequences, with one sample per row |
hmm.init |
Optional initial model, can be partially specified |
tol |
Stopping criterion: relative tolerance on the log-likelihood |
LLstop |
Stopping criterion: hard bound on the log-likelihood value |
min.iter |
At least this number of EM iterations is preformed before validation and tolerance stopping criteria are triggered |
max.iter |
Stoppint criterion: maximum number of iterations |
Learns a maximum likelihood HMM given the data
A Hidden Markov Model defined by:
mu |
[K x p] matrix of component means |
sigma |
[K x p x p] array of component covariance matrices |
pi |
[K x 1] vector of mixture coefficients |
A |
[K x K] state transition matrix with element (i,j) referring to transition from state i to state j |
Artem Sokolov Artem.Sokolov@gmail.com