Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain

M. A. J. S. van Boekel (u. a.)

ISBN 10: 1402019173 / ISBN 13: 9781402019173

Published by Springer Netherland, 2004

About this Item

Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain | M. A. J. S. van Boekel (u. a.) | Taschenbuch | v | Englisch | 2004 | Springer Netherland | EAN 9781402019173 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 102470318

Bibliographic Details

• Title: Bayesian Statistics and Quality Modelling in the Agro-Food Production Chain
• Publisher: Springer Netherland
• Publication Date: 2004
• Language: English
• ISBN 10: 1402019173
• ISBN 13: 9781402019173
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 233W x 155H x 10D millimeters 233 millimeters width by 155 millimeters height by 10 millimeters depth
• Weight: 269 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

The food market is changing from a producer-controlled to a consumer-directed market. A main driving force is consumer concern about agricultural production methods and food safety. More than before, the consumer demands transparency of the production and processing chain.
A food chain can be quite complex and the use of models has become indispensable to handle this complexity. Modelling tools are becoming increasingly important to guide the decisions for production of high-quality and safe agricultural foods. With the aid of models it becomes possible to control and predict quality attributes, so that product innovation can be done more efficiently. However, quality is an elusive concept, and there is always an aspect of subjectivity and uncertainty.
A novel approach in the agro-food chain would be to tackle subjective elements and uncertainty in modelling by using Bayesian statistics and Bayesian Belief Networks. Bayesian approaches use prior probabilities (partly accounting for subjectivity) to estimate posterior probabilities, resulting in higher accuracy than is possible with classical statistical techniques. Thus, the variability and uncertainty in data and decisions, inherent in a complex food chain, can be dealt with.