BaPreStoPro: an R package for Bayesian prediction of stochastic processes
| dc.contributor.author | Hermann, Simone | |
| dc.date.accessioned | 2016-06-07T11:49:58Z | |
| dc.date.available | 2016-06-07T11:49:58Z | |
| dc.date.issued | 2016 | |
| dc.description.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. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/35066 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-17114 | |
| dc.language.iso | en | de |
| dc.relation.ispartofseries | Discussion Paper / SFB823;28, 2016 | en |
| dc.subject | Bayesian estimation | en |
| dc.subject | stochastic differential equation | en |
| dc.subject | (jump) diffusion | en |
| dc.subject | hidden Markov model | en |
| dc.subject | hierarchical (mixed) model | en |
| dc.subject | Euler-Maruyama approximation | en |
| dc.subject.ddc | 310 | |
| dc.subject.ddc | 330 | |
| dc.subject.ddc | 620 | |
| dc.title | BaPreStoPro: an R package for Bayesian prediction of stochastic processes | en |
| dc.type | Text | de |
| dc.type.publicationtype | workingPaper | de |
| dcterms.accessRights | open access |