# sampleHMM: Sample hidden Markov models In SNPknock: Knockoffs for Hidden Markov Models and Genetic Data

## Description

This function draws independent random samples of an hidden Markov model.

## Usage

 1 sampleHMM(pInit, Q, pEmit, n = 1) 

## Arguments

 pInit an array of length K, containing the marginal distribution of the states for the first variable. Q an array of size (p-1,K,K), containing a list of p-1 transition matrices between the K states of the Markov chain. pEmit an array of size (p,M,K), containing the emission probabilities for each of the M possible emission states, from each of the K hidden states and the p variables. n the number of independent samples to be drawn (default: 1).

## Details

Each element of the output matrix is an integer value between 0 and K-1. The transition matrices contained in Q are defined with the same convention as in sampleDMC. The emission propability matrices contained in pEmit are defined such that P[X_{j}=k|H_{j}=l]=\mathrm{pEmit}[j,k,l], where H_j is the latent variable associated to X_j.

## Value

A matrix of size n-by-p containing the n observed Markov chains of length p.

## References

\insertRef

sesia2019SNPknock

Other models: sampleDMC
 1 2 3 4 5 6 7 p=10; K=5; M=3; pInit = rep(1/K,K) Q = array(stats::runif((p-1)*K*K),c(p-1,K,K)) for(j in 1:(p-1)) { Q[j,,] = Q[j,,] / rowSums(Q[j,,]) } pEmit = array(stats::runif(p*M*K),c(p,M,K)) for(j in 1:p) { pEmit[j,,] = pEmit[j,,] / rowSums(pEmit[j,,]) } X = sampleHMM(pInit, Q, pEmit, n=20)