Autor(en): | Hermann, Simone |
Titel: | BaPreStoPro: an R package for Bayesian prediction of stochastic processes |
Sprache (ISO): | en |
Zusammenfassung: | 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. |
Schlagwörter: | Bayesian estimation stochastic differential equation (jump) diffusion hidden Markov model hierarchical (mixed) model Euler-Maruyama approximation |
URI: | http://hdl.handle.net/2003/35066 http://dx.doi.org/10.17877/DE290R-17114 |
Erscheinungsdatum: | 2016 |
Enthalten in den Sammlungen: | Sonderforschungsbereich (SFB) 823 |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | |
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DP_2816_SFB823_Hermann.pdf | DNB | 664.38 kB | Adobe PDF | Öffnen/Anzeigen |
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