Calibration in a Bayesian modelling framework

M.J.W. Jansen, T.H.J. Hagenaars

    Research output: Chapter in Book/Report/Conference proceedingChapter

    Abstract

    Bayesian statistics may constitute the core of a consistent and comprehensive framework for the statistical aspects of modelling complex processes that involve many parameters whose values are derived from many sources. Bayesian statistics holds great promises for model calibration, provides the perfect starting point for uncertainty analysis and provides an excellent starting point for decision support. The purpose of this paper is to draw attention to problems and possible solutions. It is not our intention to introduce ready-for-use methods
    Original languageEnglish
    Title of host publicationBayesian Statistics and Quality Modelling in the Agro-Food Production Chain
    Editorsvan Boekel, A. Stein, van Bruggen
    Place of PublicationDordrecht
    Pages47-55
    Number of pages2
    Publication statusPublished - 2004

    Publication series

    NameWageningen UR Frontis series
    PublisherKluwer
    Numbervol. 3

    Keywords

    • bayesian theory
    • monte carlo method
    • mathematical models
    • calibration
    • uncertainty
    • decision support systems

    Fingerprint Dive into the research topics of 'Calibration in a Bayesian modelling framework'. Together they form a unique fingerprint.

    Cite this