at Google ScholarTitle data
Hartig, Florian ; Calabrese, Justin M. ; Reineking, Björn ; Wiegand, Thorsten ; Huth, Andreas:
Statistical inference for stochastic simulations models : theory and application.
In: Ecology Letters.
Vol. 14
(2011)
Issue 8
.
- pp. 816-827.
ISSN 1461-023X
DOI: https://doi.org/10.1111/j.1461-0248.2011.01640.x
Abstract in another language
Statistical models are the traditional choice to test scientific theories when observations, processes or boundary conditions are subject to stochasticity. Many important systems in ecology and biology, however, are difficult to capture with statistical models. Stochastic simulation models offer an alternative, but they were hitherto associated with a major disadvantage: their likelihood functions can usually not be calculated explicitly, and thus it is difficult to couple them to well-established statistical theory such as maximum likelihood and Bayesian statistics. A number of new methods, among them Approximate Bayesian Computing and Pattern-Oriented Modelling, bypass this limitation. These methods share three main principles: aggregation of simulated and observed data via summary statistics, likelihood approximation based on the summary statistics, and efficient sampling. We discuss principles as well as advantages and caveats of these methods, and demonstrate their potential for integrating stochastic simulation models into a unified framework for statistical modelling.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Additional notes: | BAYCEER97167 |
| Institutions of the University: | Research Institutions > Research Centres > Bayreuth Center of Ecology and Environmental Research- BayCEER Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences > Junior Professor Biogeographical Modelling Faculties Faculties > Faculty of Biology, Chemistry and Earth Sciences Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Earth Sciences Research Institutions Research Institutions > Research Centres |
| Result of work at the UBT: | Yes |
| DDC Subjects: | 500 Science |
| Date Deposited: | 29 Apr 2015 15:42 |
| Last Modified: | 15 Mar 2022 14:23 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/11681 |