Authors: Hermann, Simone
Title: BaPreStoPro: an R package for Bayesian prediction of stochastic processes
Language (ISO): en
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.
Subject Headings: Bayesian estimation
stochastic differential equation
(jump) diffusion
hidden Markov model
hierarchical (mixed) model
Euler-Maruyama approximation
Issue Date: 2016
Appears in Collections:Sonderforschungsbereich (SFB) 823

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