Sim.HMM.Gaussian.1d: Simulation of a univariate Gaussian Hidden Markov Model (HMM)

In GaussianHMM1d: Inference, Goodness-of-Fit and Forecast for Univariate Gaussian Hidden Markov Models

Simulation of a univariate Gaussian Hidden Markov Model (HMM)

Description

This function simulates observations from a univariate Gaussian HMM

Usage

Sim.HMM.Gaussian.1d(mu, sigma, Q, eta0, n)

Arguments

mu	vector of means for each regime (r x 1);
sigma	vector of standard deviations for each regime (r x 1);
Q	Transition probality matrix (r x r);
eta0	Initial value for the regime;
n	number of simulated observations.

Value

x	Simulated Data
reg	Markov chain regimes

Author(s)

Bouchra R Nasri and Bruno N Rémillard, January 31, 2019

Examples

Q <- matrix(c(0.8, 0.3, 0.2, 0.7),2,2) ; mu <- c(-0.3 ,0.7) ; sigma <- c(0.15,0.05);
sim <- Sim.HMM.Gaussian.1d(mu,sigma,Q,eta0=1,n=100)

GaussianHMM1d documentation built on July 9, 2023, 6:52 p.m.