posterior_prob_normal: Forward-Backward Algorithm to Calculate the Posterior...

In oHMMed: HMMs with Ordered Hidden States and Emission Densities

Forward-Backward Algorithm to Calculate the Posterior Probabilities of Hidden States in Normal Model

Description

Forward-Backward Algorithm to Calculate the Posterior Probabilities of Hidden States in Normal Model

Usage

posterior_prob_normal(data, pi, mat_T, means, sdev)

Arguments

data	(numeric) normal data
pi	(numeric) prior probability of states
mat_T	(matrix) transition probability matrix
means	(numeric) vector with prior means
sdev	(numeric) prior standard deviation

Details

Please see supplementary information at \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1186/s12859-024-05751-4")} for more details on the algorithm.

Value

List with the following elements:

• F: auxiliary forward variables

• B: auxiliary backward variables

• s: weights

Examples

prior_mat <- rbind(c(1-0.05, 0.05, 0),
                  c(0.05, 1-0.1, 0.05),
                  c(0, 0.05, 1-0.05))

prior_means <- c(-0.1, 0.0, 0.1)
prior_sd  <- sqrt(0.1)
L <- 100

# Simulate HMM model based on normal data based on prior information
sim_data_normal <- hmm_simulate_normal_data(L = L,
                                            mat_T = prior_mat,
                                            means = prior_means,
                                            sigma = prior_sd)
pi <- sim_data_normal$pi
# pi <- get_pi(prior_mat)
hmm_norm_data <- sim_data_normal$data

# Calculate posterior probabilities of hidden states
post_prob <-  posterior_prob_normal(data = hmm_norm_data,
                                    pi = pi,
                                    mat_T = prior_mat,
                                    means = prior_means,
                                    sdev = prior_sd)
str(post_prob)

oHMMed documentation built on May 29, 2024, 2:20 a.m.