initHMM

Function used to generate a hidden Markov model with categorical variables and random parameters. The code for the methods with continuous values or discrete data can be viewed in <code>"initGHMM"</code> and <code>"initPHMM"</code>, respectively.

initialization

Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach.

Roberto Cardenas-Ovando

RcppHMM

Rcpp Hidden Markov Model

Julieta Noguez

Claudia Rangel-Escareno

initHMM function

<dl> <dt>n</dt>
<dd>the number of hidden states to use.</dd> <dt>m</dt>
<dd>the number of possible categories (labels) generated by the hidden states.</dd></dl>

Arguments

Random Initialization for a Hidden Markov Model with emissions modeled as categorical variables — initHMM

<dl>

 <dt>n</dt>
<dd>the number of hidden states to use.</dd>

 <dt>m</dt>
<dd>the number of possible categories (labels) generated by the hidden states.</dd>

</dl>

initHMM: Random Initialization for a Hidden Markov Model with emissions modeled as categorical variables

Description

Usage

Value

Arguments

References

Examples