An MCMC computational approach for a continuous time state-dependent regime switching diffusion process.

ABSTRACT: State-dependent regime switching diffusion processes or hybrid switching diffusion (HSD) processes are hard to simulate with classical methods which leads us to adopt a Markov chain Monte Carlo (MCMC) Bayesian approach very convenient to estimate complicated models such as the HSD one. In the HSD, the diffusion component is dependent on the switching discrete hidden regimes and the transition rates of the regime switching are dependent on the diffusion observations. Since in reality phenomena are only observed in discrete times, data imputation is called for to create more observations so as to have good approximations for the density of the diffusion process. Three categories of entities will be computed in a Bayesian context: The latent imputed observations, the regime switching states,

SUBMITTER: Hibbah EH 

PROVIDER: S-EPMC9041985 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature