BaPreStoPro: an R package for Bayesian prediction of stochastic processes
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Date
2016
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Abstract
In many applications, stochastic processes are used for modeling. Bayesian
analysis is a strong tool for inference as well as for prediction. We here present
an R package for a large class of models, all based on the definition of a jump
diffusion with a non-homogeneous Poisson process. Special cases, as the Poisson
process itself, a general diffusion process or a hierarchical (mixed) diffusion model,
are considered. The package is a general tool box, because it is based on the
stochastic differential equation, approximated with the Euler scheme. Functions
for simulation, estimation and prediction are provided for each considered model.
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Keywords
Bayesian estimation, stochastic differential equation, (jump) diffusion, hidden Markov model, hierarchical (mixed) model, Euler-Maruyama approximation