| initGHMM {RcppHMM} | R Documentation |
Random Initialization for a Hidden Markov Model with emissions modeled as continuous variables
Description
Function used to generate a hidden Markov model with continuous variables and random parameters. This method allows using the univariate version of a Gaussian Mixture Model when setting m = 1. The code for the methods with categorical values or discrete data can be viewed in "initHMM" and "initPHMM", respectively.
Usage
initGHMM(n,m)
Arguments
n |
the number of hidden states to use. |
m |
the number of variables generated by the hidden states (Dimensionality of the bbserved vector). |
Value
A "list" that contains the required values to specify the model.
Model |
it specifies that the observed values are to be modeled as a Gaussian mixture model. |
StateNames |
the set of hidden state names. |
A |
the transition probabilities matrix. |
Mu |
a matrix of means of the observed variables (rows) in each states (columns). |
Sigma |
a 3D matrix that has the covariance matrix of each state. The number of slices is equal to the maximum number of hidden states. |
Pi |
the initial probability vector. |
References
Cited references are listed on the RcppHMM manual page.
Examples
n <- 3
m <- 5
model <- initGHMM(n, m)
print(model)