viterbi: Viterbi algorithm for state decoding in homogeneous HMMs

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

Viterbi algorithm for state decoding in homogeneous HMMs

Description

The Viterbi algorithm allows one to decode the most probable state sequence of an HMM.

Usage

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

Arguments

delta	initial distribution of length N, or matrix of dimension c(k,N) for k independent tracks, if trackID is provided
Gamma	transition probability matrix of dimension c(N,N) or array of transition probability matrices of dimension c(N,N,k) if trackID is provided
allprobs	matrix of state-dependent probabilities/ density values of dimension c(n, N)
trackID	optional vector of k track IDs, if multiple tracks need to be decoded separately
mod	optional model object containing initial distribution delta, transition probability matrix Gamma, matrix of state-dependent probabilities allprobs, and potentially a trackID variable If you are using automatic differentiation either with RTMB::MakeADFun or qreml and include forward in your likelihood function, the objects needed for state decoding are automatically reported after model fitting. Hence, you can pass the model object obtained from running RTMB::report() or from qreml directly to this function.

Value

vector of decoded states of length n

See Also

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

Examples

delta = c(0.5, 0.5)
Gamma = matrix(c(0.9, 0.1, 0.2, 0.8), nrow = 2, byrow = TRUE)
allprobs = matrix(runif(200), nrow = 100, ncol = 2)
states = viterbi(delta, Gamma, allprobs)

LaMa documentation built on June 8, 2025, 1:53 p.m.