multi_HMM_EM: Fitting a Hidden Markov Model via Expectation-Maximisation...
In pneff93/HMM: Fitting a Hidden Markov Model

Description Usage Arguments Details Value

Estimation of the transition probabilites, the initial state probabilites and the hidden state parameters of a Hidden Markov Model by using the Baum-Welch Algorithm.

1 2	multi_HMM_EM(x, theta, m, L1, L2, L3 = NULL, L4 = NULL, L5 = NULL, iterations = NULL, DELTA = NULL)

`x`	a sample of a Hidden Markov Model
`theta`	initial parameters for the estimation of the likelihood parameters. See details for more information
`m`	the number of states
`L1`	likelihood of the first hidden state
`L2`	likelihood of the second hidden state
`L3`	optional. likelihood of the third hidden state
`L4`	optional. likelihood of the 4th hidden state
`L5`	optional. likelihood of the 5th hidden state
`iterations`	optional. number of iterations for the EM-Algorithm
`DELTA`	optional. stop criterion for the EM-Algorithm

This function estimates the hidden states of the Hidden Markov Model with the help of the Baum Welch algorithm. The function iteratively applies both estimation and maximisation steps to arrive at the predicted parameters. When the maximal difference between present and prior parameter is abitrarily small (defined with DELTA) the iteration stops.

This function is able to calculate with multiple Theta values for the individual likelihoods. For each likelihood the right number initial starting parameter has to be set in order to compute the estimation of the corresponding Thetas. For each Likelihood the starting values must be in the format of a vector, which is then saved as a list element.

e.g.: theta[[i]] <- c(parameter1, parameter2, ...)

The function then extracts the right number of parameters per likelihood and optimizes the values.

The estimated parameters are rounded by 3 decimals and returned in a list.

pneff93/HMM documentation built on Oct. 26, 2019, 8:16 a.m.