Viterbi.hmm0norm: Viterbi Path of a 1-D HMM with Extra Zeros
In HMMextra0s: Hidden Markov Models with Extra Zeros

Description Usage Arguments Value Author(s) References Examples

Finds the most probable sequence of hidden states of an observed process.

1	Viterbi.hmm0norm(R, Z, HMMest)

`R`	is the observed data. `R` is a T 1* matrix, where T is the number of observations.
`Z`	is the binary data with the value 1 indicating that an event was observed and 0 otherwise. `Z` is a vector of length T.
`HMMest`	is a list which contains pie, gamma, sig, mu, and delta (the HMM parameter estimates).

`y`	is the estimated Viterbi path.
`v`	is the estimated probability of each time point being in each state.

Ting Wang

Wang, T., Zhuang, J., Obara, K. and Tsuruoka, H. (2016) Hidden Markov Modeling of Sparse Time Series from Non-volcanic Tremor Observations. Journal of the Royal Statistical Society, Series C, Applied Statistics, 66, Part 4, 691-715.

pie <- c(0.002,0.2,0.4)
gamma <- matrix(c(0.99,0.007,0.003,
                  0.02,0.97,0.01,
                  0.04,0.01,0.95),byrow=TRUE, nrow=3)
mu <- matrix(c(0.3,0.7,0.2),nrow=1)
sig <- matrix(c(0.2,0.1,0.1),nrow=1)
delta <- c(1,0,0)
y <- sim.hmm0norm(mu,sig,pie,gamma,delta, nsim=5000)
R <- as.matrix(y$x,ncol=1)
Z <- y$z
HMMEST <- hmm0norm(R, Z, pie, gamma, mu, sig, delta)
Viterbi3 <- Viterbi.hmm0norm(R,Z,HMMEST)