seqHMM (version 1.2.6)

forward_backward: Forward and Backward Probabilities for Hidden Markov Model

Description

The forward_backward function computes scaled forward and backward probabilities of a hidden Markov model.

Usage

forward_backward(model, forward_only = FALSE, log_space = FALSE, threads = 1)

Value

List with components

forward_probs

If log_space = FALSE, scaled forward probabilities, i.e. probability of state given observations up to that time point. If log_space = TRUE, logarithms of non-scaled forward probabilities.

backward_probs

Scaled backward probabilities (log_space = FALSE), or logarithms of non-scaled backward probabilities(log_space = TRUE).

scaling_factors

Sum of non-scaled forward probabilities at each time point. Only computed if log_space = FALSE.

In case of multiple observations, these are computed independently for each sequence.

Arguments

model

Object of class hmm or mhmm.

forward_only

If TRUE, only forward probabilities are computed. The default is FALSE.

log_space

Compute forward and backward probabilities in logarithmic scale instead of scaling. The default is FALSE.

threads

Number of threads used in parallel computing. The default is 1.

Examples

# Load a pre-defined MHMM
data("mhmm_biofam")

# Compute forward and backward probabilities
fb <- forward_backward(mhmm_biofam)

# The most probable hidden state at time t
# given the observations up to time t for the first subject:
apply(fb$forward_probs[, , 1], 2, which.max)