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Viterbi algorithm for state decoding in periodically inhomogeneous HMMs

viterbi_p.Rd

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

Usage

viterbi_p(delta, Gamma, allprobs, tod, 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

This could e.g. be the periodically stationary distribution (for each track).

Gamma

array of transition probability matrices for each time point in the cycle of dimension c(N,N,L), where L is the length of the cycle

allprobs

matrix of state-dependent probabilities/ density values of dimension c(n, N)

tod

(Integer valued) variable for cycle indexing in 1, ..., L, mapping the data index to a generalised time of day (length n)

For half-hourly data L = 48. It could, however, also be day of year for daily data and L = 365.

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_p 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_g()

Examples

delta = c(0.5, 0.5)
beta = matrix(c(-2, 1, -1,
                -2, -1, 1), nrow = 2, byrow = TRUE)
Gamma = tpm_p(1:24, 24, beta)

tod = rep(1:24, 5)
n = length(tod)

allprobs = matrix(runif(2*n), nrow = n, ncol = 2)
states = viterbi_p(delta, Gamma, allprobs, tod)