Computes $$\Pr(S_t = j \mid X_1, ..., X_T)$$ for homogeneous HMMs
stateprobs(delta, Gamma, allprobs, trackID = NULL, mod = NULL)matrix of conditional state probabilities of dimension c(n,N)
initial or stationary distribution of length N, or matrix of dimension c(k,N) for k independent tracks, if trackID is provided
transition probability matrix of dimension c(N,N), or array of k transition probability matrices of dimension c(N,N,k), if trackID is provided
matrix of state-dependent probabilities/ density values of dimension c(n, N)
optional vector of length n containing IDs
If provided, the total log-likelihood will be the sum of each track's likelihood contribution.
In this case, Gamma can be a matrix, leading to the same transition probabilities for each track, or an array of dimension c(N,N,k), with one (homogeneous) transition probability matrix for each track.
Furthermore, instead of a single vector delta corresponding to the initial distribution, a delta matrix of initial distributions, of dimension c(k,N), can be provided, such that each track starts with it's own initial distribution.
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.
Other decoding functions:
stateprobs_g(),
stateprobs_p(),
viterbi(),
viterbi_g(),
viterbi_p()
Gamma = tpm(c(-1,-2))
delta = stationary(Gamma)
allprobs = matrix(runif(200), nrow = 100, ncol = 2)
probs = stateprobs(delta, Gamma, allprobs)
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