viterbi_g: Viterbi algorithm for state decoding in inhomogeneous HMMs

In LaMa: Fast Numerical Maximum Likelihood Estimation for Latent Markov Models

Viterbi algorithm for state decoding in inhomogeneous HMMs

Description

The Viterbi algorithm decodes the most probable state sequence of an HMM.

Usage

viterbi_g(delta, Gamma, allprobs, trackID = NULL, mod = NULL)

Arguments

delta	initial distribution; either a vector of length nStates, or a matrix of dimension c(nTracks, nStates) if trackID is provided
Gamma	array of transition probability matrices of dimension c(nStates, nStates, nObs), where the first slice of each track is ignored as there is no transition into the start of a track. For a single track, an array of dimension c(nStates, nStates, nObs-1) is also accepted.
allprobs	matrix of state-dependent probabilities or density values of dimension c(nObs, nStates)
trackID	optional vector of length nObs containing nTracks unique IDs that separate tracks
mod	optional model object containing delta, Gamma, allprobs, and optionally trackID. When using RTMB::MakeADFun or qreml with forward_g in the likelihood, these are reported automatically after model fitting and the object returned by RTMB::report() or qreml can be passed directly.

Value

vector of decoded states of length nObs

See Also

Other decoding functions: stateprobs(), stateprobs_g(), stateprobs_p(), viterbi(), viterbi_p()

Examples

delta = c(0.5, 0.5)
Gamma = tpm_g(runif(10), matrix(c(-2,-2,1,-1), nrow = 2))
allprobs = matrix(runif(20), nrow = 10, ncol = 2)
states = viterbi_g(delta, Gamma[,,-1], allprobs)

LaMa documentation built on July 25, 2026, 1:08 a.m.