Probabilistic modelling: theory and practice

Probabilistic modelling techniques allow much more realistic estimates of exposure and risk by computing the use of the full range of potential exposures rather than single 'worst case' exposures. However, these techniques require additional considerations regarding the appropriate data an...

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Bibliographic Details
Published in:Food additives and contaminants 2000-07, Vol.17 (7), p.591-599
Main Author: Petersen, Barbara J.
Format: Article
Language:English
Subjects:
Biological and medical sciences
Exact sciences and technology
Food Contamination - analysis
Food industries
Fundamental and applied biological sciences. Psychology
General aspects
Humans
Logic and foundations
Mathematical logic, foundations, set theory
Mathematics
Medical sciences
Methods of analysis, processing and quality control, regulation, standards
Models, Statistical
Monte Carlo Method
Nonstandard models
Pesticide Residues - poisoning
Pesticides, fertilizers and other agrochemicals toxicology
Probabilistic Modelling Monte Carlo Analysis Exposure Precision
Risk Assessment - methods
Sciences and techniques of general use
Toxicology
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Summary:Probabilistic modelling techniques allow much more realistic estimates of exposure and risk by computing the use of the full range of potential exposures rather than single 'worst case' exposures. However, these techniques require additional considerations regarding the appropriate data and models. This article reviews the theoretical aspects of probabilistic modelling and also considers some of the practical applications. The most common method, called Monte Carlo analysis, is discussed in some detail. The practical application of Monte Carlo to risk assessments is presented along with an evaluation of the input parameters. Topics also discussed include considerations of the requirements for precision and procedures for validation of assessments.
ISSN:0265-203X
1464-5122
DOI:10.1080/026520300412500