IDEAS home Printed from https://ideas.repec.org/a/bes/jnlasa/v101y2006p1132-1143.html
   My bibliography  Save this article

Bayes Linear Calibrated Prediction for Complex Systems

Author

Listed:
  • Goldstein, Michael
  • Rougier, Jonathan

Abstract

No abstract is available for this item.

Suggested Citation

  • Goldstein, Michael & Rougier, Jonathan, 2006. "Bayes Linear Calibrated Prediction for Complex Systems," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1132-1143, September.
  • Handle: RePEc:bes:jnlasa:v:101:y:2006:p:1132-1143
    as

    Download full text from publisher

    File URL: http://www.ingentaconnect.com/content/asa/jasa/2006/00000101/00000475/art00030
    File Function: full text
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ioannis Andrianakis & Ian R Vernon & Nicky McCreesh & Trevelyan J McKinley & Jeremy E Oakley & Rebecca N Nsubuga & Michael Goldstein & Richard G White, 2015. "Bayesian History Matching of Complex Infectious Disease Models Using Emulation: A Tutorial and a Case Study on HIV in Uganda," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-18, January.
    2. Jackson Samuel E. & Vernon Ian & Liu Junli & Lindsey Keith, 2020. "Understanding hormonal crosstalk in Arabidopsis root development via emulation and history matching," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(2), pages 1-33, April.
    3. Jonathan Rougier & Martin Kern, 2010. "Predicting snow velocity in large chute flows under different environmental conditions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(5), pages 737-760, November.
    4. David Randell & Michael Goldstein & Philip Jonathan, 2014. "Bayes linear variance structure learning for inspection of large scale physical systems," Journal of Risk and Reliability, , vol. 228(1), pages 3-18, February.
    5. Nott, David J. & Marshall, Lucy & Fielding, Mark & Liong, Shie-Yui, 2014. "Mixtures of experts for understanding model discrepancy in dynamic computer models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 491-505.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bes:jnlasa:v:101:y:2006:p:1132-1143. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.