HMM_DM: Fitting a Hidden Markov Model via the Direct 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 Direct Maximisation of the global log-likelihood.

1	HMM_DM(x, m, L1, L2, L3, L4, L5)

`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

This function estimates the Hidden Markov states by maximising the normalized log-likelihood of the forward propabilities. Due to the fact that both the Gamma matrix as well as the Delta vector have some constraints, the function first applies some restrictions and then uses the base-R maximisation to gain the most likely variables.

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.