hmhmm: Estimation of a hidden Markov model with 2 hidden and 4 observed states
Description
This function implements a Metropolis within Gibbs algorithm that produces
a sample on the parameters \(p_{ij}\) and \(q^i_j\) of the hidden Markov
model (Chapter 7). It includes a function likej that computes the likelihood of
the times series using a forward-backward algorithm.
Usage
hmhmm(M = 100, y)
Value
BigR
matrix of the iterated values returned by the MCMC algorithm containing
\(p_{11}\) and \(p_{22}\), transition probabilities, and
\(q^1\) and \(q^2\), vector of probabilities for both latent states
olike
sequence of the log-likelihoods produced by the MCMC sequence
Arguments
M
Number of Gibbs iterations
y
times series to be modelled by a hidden Markov model
Details
The Metropolis-within-Gibbs step involves Dirichlet proposals with
a random choice of the scale between 1 and 1e5.