Food
and
Organization
of
the
United
Nations
COMPUTERIZED INFORMATION SERIES
fisheries
Andre E. Punt
and
Ray Hilborn
School of Aquatic and Fishery Sciences
University of Washington
Seattle, Washington, USA
|
|
Food |
FOOD AND ORGANIZATION OF THE UNITED NATIONS
Rome, 2001
|
The designation employed and the presentation of material in
this information product do not imply the expression of any opinion whatsoever
on the part of the Food and Organization of the United Nations concerning the
legal status of any country, territory, city or area or of its authorities, or
concerning the delimitation of its frontiers or boundaries. |
ISBN 92-5-104640-9
All rights reserved. Reproduction and dissemination of material in this information product for educational or other non-commercial purposes are authorized without any prior written permission from the copyright holders provided the source is fully acknowledge. Reproduction of material in this information product for resale or other commercial purposes is prohibited without written permission of the copyright holders. Applications for such permission should be addressed to the copyright holders. Applications for such permission should be addressed to the Chief, Publishing and Multimedia Service, Information Division, FAO, Vialle delle Terme di Caracalla, 00100 Rome, Italy or by e-mail to [email protected].
© FAO 2001
1.1 The decision table
1.2 Elements of a decision analysis1.2.1 Identifying the alternative hypotheses
1.2.2 Determining the weight of evidence
1.2.3 Specifying the alternative management actions
1.2.4 Specifying the performance statistics
1.2.5 Calculating the values of the performance statistics
1.2.6 Presenting the results to the decision makers1.3.1 Likelihood
1.3.2 The prior distribution
1.3.3 Bayes rule
2. METHODS FOR COMPUTING POSTERIOR DISTRIBUTIONS
2.1 Grid search
2.2 The SIR method
2.3 The Markov Chain Monte Carlo method
2.4 Diagnostics
2.5 Marginal distributions
2.6 Advanced topics2.6.1 Selecting a subset of the parameter vectors
2.6.2 A more efficient SIR
2.6.3 An advanced version of MCMC
2.6.4 Two additional techniques for reducing computation time
3.1 Biomass dynamics model (spreadsheets EX4A.XLS and EX4B.XLS)
3.2 Stock recruitment analysis (spreadsheets EX4C.XLS and EX4D.XLS)
3.3.1 Basic dynamics equations
3.3.2 Fitting to data
3.3.3 A worked example
4. DETERMINING PRIOR DISTRIBUTIONS
4.1 General issues
4.2 Expert opinion
4.3 Data summaries/meta-analysis
4.4 Default options for parameters and their priors4.4.1 Biomass dynamics models
4.4.2 Age-structured/delay-difference models
4.4.3 Stock-recruitment models
5. STRENGTHS AND WEAKNESSES OF THE BAYESIAN APPROACH
5.1 Why use Bayesian methods?
5.2 Overcoming problems with prior distributions
5.3 The computational demands
5.4 In conclusion