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

## Description

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

## Usage

 `1` ```Viterbi.hmm0norm(R, Z, HMMest) ```

## Arguments

 `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).

## Value

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

Ting Wang

## References

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.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```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) ```

HMMextra0s documentation built on Aug. 3, 2021, 9:06 a.m.