tpm_phsmm: Builds all transition probability matrices of an...

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

Builds all transition probability matrices of an periodic-HSMM-approximating HMM

Description

Hidden semi-Markov models (HSMMs) are a flexible extension of HMMs. For direct numerical maximum likelhood estimation, HSMMs can be represented as HMMs on an enlarged state space (of size M) and with structured transition probabilities.

This function computes the transition matrices of a periodically inhomogeneos HSMMs.

Usage

tpm_phsmm(omega, dm, eps = 1e-10)

Arguments

omega	embedded transition probability matrix Either a matrix of dimension c(N,N) for homogeneous conditional transition probabilities (as computed by tpm_emb), or an array of dimension c(N,N,L) for inhomogeneous conditional transition probabilities (as computed by tpm_emb_g).
dm	state dwell-time distributions arranged in a list of length N Each list element needs to be a matrix of dimension c(L, N_i), where each row t is the (approximate) probability mass function of state i at time t.
eps	rounding value: If an entry of the transition probabily matrix is smaller, than it is rounded to zero. Usually, this should not be changed.

Value

list of dimension length L, containing sparse extended-state-space transition probability matrices of the approximating HMM for each time point of the cycle.

Examples

N = 2 # number of states
L = 24 # cycle length
# time-varying mean dwell times
Z = trigBasisExp(1:L) # trigonometric basis functions design matrix
beta = matrix(c(2, 2, 0.1, -0.1, -0.2, 0.2), nrow = 2)
Lambda = exp(cbind(1, Z) %*% t(beta))
sizes = c(20, 20) # approximating chain with 40 states
# state dwell-time distributions
dm = lapply(1:N, function(i) sapply(1:sizes[i]-1, dpois, lambda = Lambda[,i]))

## homogeneous conditional transition probabilites
# diagonal elements are zero, rowsums are one
omega = matrix(c(0,1,1,0), nrow = N, byrow = TRUE)

# calculating extended-state-space t.p.m.s
Gamma = tpm_phsmm(omega, dm)

## inhomogeneous conditional transition probabilites
# omega can be an array
omega = array(omega, dim = c(N,N,L))

# calculating extended-state-space t.p.m.s
Gamma = tpm_phsmm(omega, dm)

LaMa documentation built on Nov. 5, 2025, 6:42 p.m.