dmvnorm.hsmm: Emission ensity function for a multivariate normal emission...

In mhsmm: Inference for Hidden Markov and Semi-Markov Models

Emission ensity function for a multivariate normal emission distribution

Description

Calculates the density of observations x for state j given the parameters in model. This is used for a multivariate Gaussian emission distribution of a HMM or HSMM and is a suitable prototype for user's to make their own custom distributions.

Usage

dmvnorm.hsmm(x, j, model)

Arguments

x	Observed value
j	State
model	A hsmmspec or hmmspec object

Details

This is used by hmm and hsmm to calculate densities for use in the E-step of the EM algorithm. It can also be used as a template for users wishing to building their own emission distributions

Value

A vector of probability densities.

Author(s)

Jared O'Connell [e-mail]

See Also

mstep.mvnorm, rmvnorm.hsmm

Examples

  J<-2
  initial <- rep(1/J,J)
  P <- matrix(c(.3,.5,.7,.5),nrow=J)
  b <- list(mu=list(c(-3,0),c(1,2)),sigma=list(diag(2),matrix(c(4,2,2,3), ncol=2)))
  model <- hmmspec(init=initial, trans=P, parms.emission=b,dens.emission=dmvnorm.hsmm)
  model
  train <- simulate(model, nsim=300, seed=1234, rand.emis=rmvnorm.hsmm)
  plot(train,xlim=c(0,100))
  h1 = hmmfit(train,model,mstep=mstep.mvnorm)

mhsmm documentation built on Aug. 23, 2023, 9:06 a.m.