BaumWelch {HiddenMarkov}R Documentation

Estimate Parameters Using Baum-Welch Algorithm

Description

Estimates the parameters of a hidden Markov model. The Baum-Welch algorithm (Baum et al, 1970) referred to in the HMM literature is a version of the EM algorithm (Dempster et al, 1977). See Hartley (1958) for an earlier application of the EM methodology, though not referred to as such.

Usage

BaumWelch(object, control, ...)
## S3 method for class 'dthmm':
BaumWelch(object, control = bwcontrol(), ...)
## S3 method for class 'mmglm':
BaumWelch(object, control = bwcontrol(), ...)
## S3 method for class 'mmpp':
BaumWelch(object, control = bwcontrol(), ...)

Arguments

object an object of class "dthmm", "mmglm", or "mmpp".
control a list of control settings for the iterative process. These can be changed by using the function bwcontrol.
... other arguments.

Details

The initial parameter values used by the EM algorithm are those that are contained within the input object.

The code for the methods "dthmm", "mmglm" and "mmpp" can be viewed by typing BaumWelch.dthmm, BaumWelch.mmglm or BaumWelch.mmpp, respectively, on the R command line.

Value

The output object (a list) with have the same class as the input, and will have the same components. The parameter values will be replaced by those estimated by this function. The object will also contain additional components.
An object of class "dthmm" will also contain

u an n times m matrix containing estimates of the conditional expectations. See “Details” in Estep.
v an n times m times m array containing estimates of the conditional expectations. See “Details” in Estep.
LL value of log-likelihood at the end.
iter number of iterations performed.
diff difference between final and previous log-likelihood.

See Also

logLik, residuals, simulate, summary , neglogLik


[Package HiddenMarkov version 1.2-7 Index]