A future for models and data in environmental science.

Published

Journal Article (Review)

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

Pubmed Central 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