BAYES-SA - Bayesian Stock Assessment Methods in Fisheries - User's Manual













Table of Contents


COMPUTERIZED INFORMATION SERIES
fisheries

Andre E. Punt
and
Ray Hilborn

School of Aquatic and Fishery Sciences
University of Washington
Seattle, Washington, USA


Food
and
Organization
of
the
United
Nations

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

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© FAO 2001


Table of Contents


PREPARATION OF THIS DOCUMENT

ABSTRACT

1. INTRODUCTION

1.1 The decision table
1.2 Elements of a decision analysis

1.2.1 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 makers

1.3 Review of basic tools

1.3.1 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 topics

2.6.1 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. WORKED EXAMPLES

3.1 Biomass dynamics model (spreadsheets EX4A.XLS and EX4B.XLS)

3.1.1 Stock assessment phase
3.1.2 Decision analysis phase

3.2 Stock recruitment analysis (spreadsheets EX4C.XLS and EX4D.XLS)

3.2.1 Stock assessment phase
3.2.2 Decision analysis phase

3.3 Age-structured models

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 priors

4.4.1 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

6. ACKNOWLEDGEMENTS

7. GLOSSARY

8. THE MACROS

9. LITERATURE CITED

FAO - COMPUTERIZED INFORMATION SERIES fisheries

BAYES-SA - Bayesian stock assessment methods in fisheries