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Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and Inputs 1999 ed. [Mīkstie vāki]

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  • Formāts: Paperback / softback, 336 pages, height x width: 235x155 mm, weight: 1090 g, XVI, 336 p., 1 Paperback / softback
  • Izdošanas datums: 31-Jul-1999
  • Izdevniecība: Kluwer Academic/Plenum Publishers
  • ISBN-10: 0306459574
  • ISBN-13: 9780306459573
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Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and Inputs 1999 ed.Palielināt
  • Formāts: Paperback / softback, 336 pages, height x width: 235x155 mm, weight: 1090 g, XVI, 336 p., 1 Paperback / softback
  • Izdošanas datums: 31-Jul-1999
  • Izdevniecība: Kluwer Academic/Plenum Publishers
  • ISBN-10: 0306459574
  • ISBN-13: 9780306459573
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780306459573.html
Keywords:
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.

Recenzijas

`...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)

Papildus informācija

Springer Book Archives
Introduction
1(14)
Background
2(2)
Historical Context for Probabilistic Exposure Assessment
4(3)
When Is Probabilistic Analysis Useful and/or Justified?
7(1)
Assessing the Existing Information Base
8(2)
Structure of the Text
10(2)
Composition of Exposure Assessment Teams
12(1)
Conventions Used in This Text
12(1)
Terminology
12(1)
Use of Examples
13(1)
Scope
13(1)
Summary of Goals and Philosophy
13(2)
A Basic Framework for Probabilistic Analysis
15(22)
Philosophy of Probabilistic Analysis
15(6)
The Frequentist View of Probability
15(3)
Subjective or Bayesian Views of Probability
18(2)
Discussion
20(1)
Taxonomy of Variability and Uncertainty
21(12)
Model Uncertainty
21(9)
Variability
30(1)
Input Uncertainty
31(2)
Comparison of Variability and Uncertainty
33(4)
Approaches to Model Uncertainty
37(22)
Purpose of Using Models
38(2)
Model Complexity
40(7)
Uncertainty Propagation Characteristics of Models
41(3)
When Is a Complex Model Needed?
44(1)
Simplifying a Complex Model
45(2)
Setting Up the Right Problem
47(1)
Model Verification
48(2)
Model Validation
50(3)
Validating with Data: The Elusive Ideal
50(2)
Partial Validation: When You Have Some Data
52(1)
What If There Are No Data?
52(1)
Can You Validate by Comparing Models?
53(1)
Extrapolation and Uncertainty
53(1)
Can Input Uncertainties Reflect Model Uncertainty?
54(1)
What Do You Do When Models Disagree?
55(1)
Examples of Model Uncertainty Issues
56(3)
Characterizing Variability and Uncertainty in Model Inputs
59(22)
Data Availability and Characteristics
59(3)
Temporal Variability
60(1)
Spatial Variability
61(1)
Interindividual Variability
61(1)
Uncertainty
62(1)
Deciding How Far to Go
62(2)
Families and Models for Probability Distributions
64(17)
Theoretical Basis for Probability Distribution Models
64(14)
Characterizing Uncertainty in Distribution Parameters
78(3)
Data and Distributions
81(80)
Summary Statistics
82(7)
Central Tendency
82(3)
Spread
85(2)
Shape
87(2)
Empirical Basis for Selecting a Parametric Probability Distribution Model
89(3)
Empirical Cumulative Distribution Functions
92(4)
Plotting Position
93(1)
Examples of Empirical Cumulative Distributions
94(2)
Other Ways to Visualize Data
96(3)
Uncertainty in Summary Statistics
99(23)
Uncertainty in the Mean
99(8)
Uncertainty in the Variance and Standard Deviation
107(5)
Uncertainty in Skewness
112(1)
Uncertainty in Kurtosis
112(1)
Multivariate Distributions for Uncertainty in Statistics
113(3)
Small Data Sets
116(2)
Summary of Uncertainty in Statistics for the PCB Concentration Data Sets
118(4)
Probability Distribution Models
122(3)
Method of Matching Moments Estimates of Distribution Parameters
122(1)
Maximum Likelihood Estimates of Distribution Parameters
122(1)
Examples of the Use of Statistical Estimators
123(2)
Probability Plots
125(18)
Empirical Basis for Selecting Probability Models
126(1)
Methods for Probability Plotting
127(2)
Identifying Distribution Types Based upon Probability Plotting
129(6)
Normal and Lognormal Probability Plots
135(1)
Estimating Distribution Parameters Using Probability Plots
135(2)
Probability Plots for the Weibull Distribution
137(1)
Probability Plots and Censored Data Sets
138(1)
Percentile-Percentile Plots
139(3)
Frequency Comparisons
142(1)
Goodness-of-Fit Tests
143(18)
Chi-Squared Test
144(5)
Kolmogorov--Smirnov Test
149(6)
Anderson--Darling Test
155(6)
Special Topics Related to Distribution Development
161(20)
Initial Distributions
161(2)
Suggested Information to Accompany Initial Distributions
162(1)
Maximum Entropy Inference Approach
163(2)
The Basis of Maximum Entropy Inference
163(1)
Example
164(1)
Combining Information
165(7)
What to Combine?
167(1)
Why Combine Information?
168(1)
When to Consider Preserving Separate Sources of Information?
169(1)
An Example
169(3)
Surprise
172(3)
Defining Surprise
173(1)
Accounting for Surprise
174(1)
Dependence among Inputs
175(2)
Expert Elicitation
177(4)
Sources of Bias in Judgments about Uncertainty
178(1)
Elicitation Protocols
178(3)
Probabilistic Modeling Techniques
181(62)
Implications of the Central Limit Theorem for Propagation of Distributions
181(2)
Properties of the Mean and Variance
183(1)
Analytical Methods: transformation of Variables
184(2)
Approximation Methods Based upon Taylor Series Expansions
186(8)
Numerical Simulation Methods
194(23)
Monte Carlo Simulation
196(11)
Latin Hypercube Sampling
207(6)
Other Sampling Techniques
213(1)
Selecting a Sample Size for Numerical Simulations
213(2)
Verification of Monte Carlo Results
215(1)
Available Software Tools
216(1)
Two-Dimensional Simulations
217(20)
Background on Two-Dimensional Methods
217(2)
Distinctions between Variability and Uncertainty in Simulations
219(1)
Characterization of Uncertainty as an Aid to Model Validation
219(2)
Taxonomy of Variability and Uncertainty in Model Inputs
221(2)
General Approach for Simulation of Variability and Uncertainty
223(1)
Developing Input Assumptions for Two-Dimensional Analyses
224(12)
Simulating Correlations among Frequency Distributions for Variability in Two-Dimensional Analyses
236(1)
Discussion of Analytical, Approximation, and Numerical Methods
237(3)
Assessing Model Uncertainties
240(1)
Uncertainty Reduction Techniques
241(2)
Identifying Key Constributors to Variability and Uncertainty in Model Outputs
243(28)
Introduction to the Examples
244(3)
Case A
245(1)
Case B
245(2)
Methods to Use Prior to Simulation
247(2)
Apportioning Variance by the Gaussian Approximation
247(1)
Coefficient of Variation
248(1)
Methods to Use after Simulation
249(19)
Scatter Plot
249(4)
Correlation Coefficient
253(10)
Correlation Ratio
263(1)
Multivariate Linear Regression
264(2)
Nominal Range Sensitivity
266(1)
Probabilistic Sensitivity Analysis
267(1)
Contribution to Variance
268(1)
Screening and Iteration
268(1)
Summary
269(2)
An Example of Probabilistic Exposure Assessment in the Community Surrounding New Bedford Harbor
271(38)
Defining the Exposure Scenario
271(6)
Temporal Scope
272(2)
Spatial/Population Scope
274(1)
Model Structure
274(3)
Distribution Development for Exposure Model Inputs
277(26)
Correlation
277(1)
Concentration of PCBs in Environmental Media
278(14)
Human Physical Characteristics
292(4)
Inputs Related to Intake and Consumption Rates
296(5)
Setup of Analysis
301(2)
Results
303(6)
One-Dimensional Analysis of Variability
303(1)
Two-Dimensional Analysis of Variability and Uncertainty
304(2)
Identification of Key Contributors to Variability in Output
306(3)
Glossary 309(8)
References 317(10)
Index 327