A future for models and data in environmental science.
Journal Article (Review;Journal Article)
Together, graphical models and the Bayesian paradigm provide powerful new tools that promise to change the way that environmental science is done. The capacity to merge theory with mechanistic understanding and empirical evidence, to assimilate diverse sources of information and to accommodate complexity will transform the collection and interpretation of data. As we discuss here, we specifically expect a shift from a focus on simple experiments with inflexible design and selection among models that embrace parts of processes to a synthesis of integrated process models. With this potential come new challenges, including some that are specific and technical and others that are general and will involve reexamination of the role of inference and prediction.
Full Text
Duke Authors
Cited Authors
- Clark, JS; Gelfand, AE
Published Date
- July 2006
Published In
Volume / Issue
- 21 / 7
Start / End Page
- 375 - 380
PubMed ID
- 16815437
Electronic International Standard Serial Number (EISSN)
- 1872-8383
International Standard Serial Number (ISSN)
- 0169-5347
Digital Object Identifier (DOI)
- 10.1016/j.tree.2006.03.016
Language
- eng