Calibration in a Bayesian modelling framwork

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

Calibration in a Bayesian modelling framwork

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Academic › peer-review

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 language	English
Title of host publication	Proceedings of the Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain, Wageningen, The Netherlands, 01-14 May 2003
Editors	M.A.J.S. Boekel, A. Stein, A.H.C. van Bruggen
Place of Publication	Dordrecht
Publisher	Kluwer Academic Publishers
Pages	47-55
ISBN (Print)	9781402019173
Publication status	Published - 2004
Event	Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain - Duration: 1 May 2003 → 14 May 2003

Workshop

Workshop	Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain
Period	1/05/03 → 14/05/03

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Jansen, M. J. W., & Hagenaars, T. H. J. (2004). Calibration in a Bayesian modelling framwork. In M. A. J. S. Boekel, A. Stein, & A. H. C. V. Bruggen (Eds.), Proceedings of the Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain, Wageningen, The Netherlands, 01-14 May 2003 (pp. 47-55). Kluwer Academic Publishers. https://edepot.wur.nl/36081

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title = "Calibration in a Bayesian modelling framwork",

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",

author = "M.J.W. Jansen and T.H.J. Hagenaars",

year = "2004",

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editor = "M.A.J.S. Boekel and A. Stein and Bruggen, {A.H.C. van}",

booktitle = "Proceedings of the Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain, Wageningen, The Netherlands, 01-14 May 2003",

publisher = "Kluwer Academic Publishers",

note = "Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain ; Conference date: 01-05-2003 Through 14-05-2003",

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Jansen, MJW & Hagenaars, THJ 2004, Calibration in a Bayesian modelling framwork. in MAJS Boekel, A Stein & AHCV Bruggen (eds), Proceedings of the Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain, Wageningen, The Netherlands, 01-14 May 2003. Kluwer Academic Publishers, Dordrecht, pp. 47-55, Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain, 1/05/03. <https://edepot.wur.nl/36081>

Calibration in a Bayesian modelling framwork. / Jansen, M.J.W.; Hagenaars, T.H.J.

Proceedings of the Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain, Wageningen, The Netherlands, 01-14 May 2003. ed. / M.A.J.S. Boekel; A. Stein; A.H.C. van Bruggen. Dordrecht : Kluwer Academic Publishers, 2004. p. 47-55.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Academic › peer-review

TY - GEN

T1 - Calibration in a Bayesian modelling framwork

AU - Jansen, M.J.W.

AU - Hagenaars, T.H.J.

PY - 2004

Y1 - 2004

N2 - 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

AB - 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

M3 - Conference paper

SN - 9781402019173

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EP - 55

BT - Proceedings of the Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain, Wageningen, The Netherlands, 01-14 May 2003

A2 - Boekel, M.A.J.S.

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A2 - Bruggen, A.H.C. van

PB - Kluwer Academic Publishers

CY - Dordrecht

T2 - Frontis Workshop on Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain

Y2 - 1 May 2003 through 14 May 2003

ER -

Calibration in a Bayesian modelling framwork

Abstract

Workshop

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