stateprobs_g: Calculate conditional local state probabilities for...

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

Calculate conditional local state probabilities for inhomogeneous HMMs

Description

Computes

\Pr(\text{State}_t = j \mid X_1, ..., X_T)

for inhomogeneous HMMs

Usage

stateprobs_g(
  delta,
  Gamma,
  allprobs,
  trackID = NULL,
  mod = NULL,
  forecast = FALSE
)

Arguments

delta	initial distribution; either a vector of length nStates, or a matrix of dimension c(nTracks, nStates) if trackID is provided
Gamma	array of transition probability matrices of dimension c(nStates, nStates, nObs), where the first slice of each track is ignored as there is no transition into the start of a track. For a single track, an array of dimension c(nStates, nStates, nObs-1) is also accepted.
allprobs	matrix of state-dependent probabilities or density values of dimension c(nObs, nStates)
trackID	optional vector of length nObs containing nTracks unique IDs that separate tracks
mod	optional model object containing delta, Gamma, allprobs, and optionally trackID. When using RTMB::MakeADFun or qreml with forward_g in the likelihood, these are reported automatically after model fitting and the object returned by RTMB::report() or qreml can be passed directly.
forecast	logical, indicating if forecast probabilities \Pr(\text{State}_t = j \mid X_1, ..., X_{t-1}) should be calculated instead.

Value

matrix of conditional state probabilities of dimension c(nObs, nStates)

See Also

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

Examples

Gamma = tpm_g(runif(10), matrix(c(-1,-1,1,-2), nrow = 2, byrow = TRUE))
delta = c(0.5, 0.5)
allprobs = matrix(runif(20), nrow = 10, ncol = 2)

probs = stateprobs_g(delta, Gamma[,,-1], allprobs)

LaMa documentation built on July 25, 2026, 1:08 a.m.