LaMa (version 2.0.3)

viterbi: 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)

Value

vector of decoded states of length n

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.

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)