HMM_EM: Fitting a Hidden Markov Model via Expectation-Maximisation...

In pneff93/HMM: Fitting a Hidden Markov Model

Description

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.

Usage

HMM_EM(x, m, L1, L2, L3 = NULL, L4 = NULL, L5 = NULL,
  iterations = NULL, DELTA = NULL)

Arguments

x	a sample of a Hidden Markov Model
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

Details

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.

Value

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.