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Viterbi algorithm for state decoding in inhomogeneous HMMs

viterbi_g.Rd

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

Usage

viterbi_g(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

array of transition probability matrices of dimension c(N,N,n-1), as in a time series of length n, there are only n-1 transitions

If an array of dimension c(N,N,n) is provided for a single track, the first slice will be ignored.

If trackID is provided, Gamma needs to be an array of dimension c(N,N,n), where n is the number of rows in allprobs. Then for each track the first transition matrix will be ignored.

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_g 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(), viterbi_p()

Examples

delta = c(0.5, 0.5)
Gamma = array(dim = c(2,2,99))
for(t in 1:99){
  gammas = rbeta(2, shape1 = 0.4, shape2 = 1)
  Gamma[,,t] = matrix(c(1-gammas[1], gammas[1], 
                      gammas[2], 1-gammas[2]), nrow = 2, byrow = TRUE)
}
allprobs = matrix(runif(200), nrow = 100, ncol = 2)
states = viterbi_g(delta, Gamma, allprobs)