hmm-learn {stochmod}R Documentation

Hidden Markov Models

Description

Maximum Likelihood learning via EM

Usage

HMM.learn( xL, K, vL=NULL, hmm.init=NULL, tol=1e-03, LLstop=Inf,
min.iter=3, max.iter=Inf )

Arguments

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

Details

Learns a maximum likelihood HMM given the data

Value

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

Author(s)

Artem Sokolov Artem.Sokolov@gmail.com


[Package stochmod version 1.2 Index]