Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and Inputs by Alison C. Cullen

Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and…

byAlison C. Cullen, H. Christopher Frey

Paperback | July 31, 1999

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At this time when regulatory agencies are accepting and actively encouraging probabilistic approaches and the attribution of overall uncertainty among inputs to support Value of Information analyses, a comprehensive sourcebook on methods for addressing variability and uncertainty in exposure analysis is sorely needed. This need is adroitly met in Probabilistic Techniques in Exposure Assessment. A host of expert contributors provide a straightforward introduction to the practical tools for addressing variability and uncertainty in support of environmental and human health decision making. 151 graphs, plots, charts, and figures supplement a broad range of detailed and practical examples.

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Title:Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and…Format:PaperbackDimensions:352 pages, 9.25 × 6.1 × 0.27 inPublished:July 31, 1999Publisher:Springer USLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0306459574

ISBN - 13:9780306459573

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Table of Contents

Introduction. A Basic Framework for Probabilistic Analysis. Approaches to Model Uncertainty. Characterizing Variability and Uncertainty in Model Inputs. Data and Distributions. Special Topics Related to Distribution Development. Probabilistic Modeling Techniques. Identifying Key Contributors to Variability and Uncertainty in Model Outputs. An Illustrative Case Study of the Application of Probabilistic Exposure Assessment. Glossary. Index.

Editorial Reviews

`...the various topics are presented extremely well from the pedagogic point of view. For readers of this journal, the most appealing feature of the book is its extensive coverage of systems modeling and simulation . Other good features of the book include: careful examination of assumptions under which probabilistic modeling is a useful and justified; excellent discussion of the many aspects of uncertainty and variability; unusually good and extensive (80-page long) coverage of probability distributions; interesting and well-described examples and the case studies in the area of risk analysis; and a useful glossary of technical terms. In summary, this is an excellent book for anyone interested in probabilistic modeling. In my opinion, it is the right book in this domain for readers of this journal and I highly recommend it.' International Journal of General Systems, 31(1):97-110, (2002)