BaPreStoPro: an R package for Bayesian prediction of stochastic processes

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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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Bayesian estimation, stochastic differential equation, (jump) diffusion, hidden Markov model, hierarchical (mixed) model, Euler-Maruyama approximation

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