Bayesian stock assessment using catch-age data and the sampling - Importance resampling algorithm

Canadian Journal of Fisheries and Aquatic Sciences

Volumn 54, Issue 2, 1997, Pages 284-300

  McAllister, Murdoch K ^a,c   Ianelli, James N ^b  

^a UNIVERSITY OF WASHINGTON   (United States)

^b ALASKA FISHERIES SCIENCE CENTER   (United States)

^c IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN APPROACH; FISHERY MANAGEMENT; IMPORTANCE RESAMPLING ALGORITHM; SAMPLING; SAMPLING-IMPORTANCE RESAMPLING ALGORITHM; STOCK ASSESSMENT;

EID: 0030619241     PISSN: 0706652X     EISSN: None     Source Type: Journal    
DOI: 10.1139/cjfas-54-2-284     Document Type: Article

References (48)

1. Arnold, S.F. 1990. Mathematical statistics. Prentice Hall Inc., Englewood Cliffs, N.J.
2. Bence, J.R., Gordoa, A., and Hightower, J.E. 1993. Influence of age-selective surveys on stock synthesis assessments. Can. J. Fish. Aquat. Sci. 50: 827-840.
3. Berger, J.O. 1985. Statistical decision theory and Bayesian analysis. 2nd ed. Springer-Verlag, New York.
4. Box, G.E.P., and Tiao, G.C. 1973. Bayesian inference in statistical analysis. Addison-Wesley, Reading, Mass.
5. Clark, W.G. 1991. Groundfish exploitation rates based on life history parameters. Can. J. Fish. Aquat. Sci. 48: 734-750.
6. Engås, A., and Ona, E. 1990. Day and night fish distribution pattern in the net mouth area of the Norwegian bottom-sampling trawl. Rapp. P.-V. Reun. Cons. Int. Explor. Mer, 189: 123-127.
7. Francis, R.I.C.C. 1992. Use of risk analysis to assess fishery management strategies: a case study using orange roughy (Hoplostethus atlanticus) on the Chatham Rise, New Zealand. Can. J. Fish. Aquat. Sci. 49: 922-930.
8. Francis, R.I.C.C., Robertson, D.A., Clark, M.R., and Coburn, R.P. 1992. Assessment of the QMA 3B orange roughy fishery for the 1992/93 season. N.Z. Fish. Assess. Res. Doc. No. 92/4.
9. Gelfand, A.E., and Smith, A.F.M. 1990. Sampling-based approaches to calculating marginal densities. J. Am. Stat. Assoc. 85: 398-409.
10. Geweke, J.F. 1989. Bayesian inference in econometric models using Monte Carlo integration. Econometrica, 57: 1317-1340.
11. Givens, G.H. 1993a. Contribution to discussion on the meeting on 'The Gibbs sampler and other Markov chain Monte Carlo methods.' J. R. Stat. Soc. B, 55: 75-76.
12. Givens, G.H. 19936. A Bayesian framework and importance sampling methods for synthesizing multiple sources of evidence and uncertainties linked by a complex mechanistic model. Ph.D. thesis, Department of Statistics, University of Washington, Seattle, Wash.
13. Gunderson, D.R. 1993. Surveys of fisheries resources. John Wiley & Sons, Inc., New York.
14. Harden-Jones, F.R., Margetts, A.R., Greer Walker, M., and Arnold, G.P. 1977. The efficiency of the Granton otter trawl determined by sector-scanning sonar and acoustic transponding tags. Rapp. P.-V. Reun. Cons. Int. Explor. Mer, 170: 45-51.
15. Hastings, W.K. 1970. Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57: 97-109.
16. Hilborn, R., and Walters, C.J. 1992. Quantitative fisheries stock assessment: choice, dynamics, and uncertainty. Chapman and Hall, New York.
17. Hoff, R.Z. 1989. Spatial distributions and abundance trends for two species of flatfish in the eastern Bering Sea. M.S. thesis, University of Washington, Seattle, Wash.
18. Ianelli, J., and Heifetz, J. 1995. Decision analysis of alternative harvest policies for the Gulf of Alaska Pacific ocean perch fishery. Fish. Res. (Amsterdam), 24: 35-63.
19. Kimura, D.K. and Lyons, J.J. 1991. Between-reader bias and variability in the age-determination process. Fish. Bull. 89: 53-60.
20. Kinas, P.G. 1993. Bayesian statistics for fishery stock assessment and management. Ph.D. thesis, Department of Statistics, University of British Columbia, Vancouver, B.C.
21. Kinas, P.G. 1996. Bayesian fishery stock assessment and decision making using adaptive importance sampling. Can. J. Fish. Aquat. Sci. 53: 414 423.
22. McAllister, M.K. 1995. Using decision analysis to choose a design for surveying fisheries resources. Ph.D. thesis, School of Fisheries, University of Washington, Seattle, Wash.
23. McAllister, M.K., and Pikitch, E.K. 1997. A Baycsian approach to choosing a design for surveying fishery resources: application to the eastern Bering Sea trawl survey. Can. J. Fish. Aquat. Sci. 54: 301-311.
24. McAllister, M.K., Pikitch, E.K., Punt, A.E., and Hilborn, R. 1994. A Bayesian approach to stock assessment and harvest decisions using the sampling/importance resampling algorithm. Can. J. Fish. Aquat. Sci. 51: 2673-2687.
25. Methot, R.D. 1990. Synthesis model: an adaptable framework for analysis of diverse stock assessment data. In Proceedings of the Symposium on Applications of Stock Assessment Techniques to Gadids. Edited by L. Low. Int. North Pac. Fish. Comm. Bull. 50: 259-277.
26. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., and Teller, E. 1953. Equations of state calculations by fast computing machines. J. Chem. Phys. 21: 1087-1092.
27. Myers, R.A., Bridson, J., and Barrowman, N.J. 1993. Summary of worldwide stock and recruitment data. Can. Tech. Rep. Fish. Aquat. Sci. No. 2024.
28. Nash, J.C. 1990. Compact numerical methods for computers: linear algebra and function minimization. 2nd ed. Adam Higler Ltd., New York.
29. Newton, M.A., and Raftery, A.E. 1994. Approximate Bayesian inference with the weighted likelihood bootstrap. J. R. Stat. Soc. B, 56: 3-48.
30. Oh, M.S., and Berger, J.O. 1992. Adaptive importance sampling in Monte Carlo integration. J. Stat. Comput. Simul. 41: 143-168.
31. Pennington, M., and Vølstad, J.H. 1994. Assessing the effect of intra-haul correlation and variable density on estimates of population characteristics from marine surveys. Biometrics, 50: 725-732.
32. Press, W.H., Teukolsky, S.A., Vetterling, W.T., and Flannery B.P. 1992. Numerical recipes in Fortran. The art of scientific computing. 2nd ed. Cambridge University Press, New York.
33. Punt, A.E. 1993. The implications of some multiple stock hypotheses for Chatham Rise orange roughy. N.Z. Fish. Assess. Res. Doc. No. 93/16.
34. Punt, A.E., Butterworth, D.S., and Penny, A.J. 1993. Stock assessment and risk analysis for the South Atlantic population of albacore (Thunnus alalunga). ICCAT Doc. No. SCRS/93/83. International Commission for the Conservation of Atlantic Tunas, La Jolla, Calif.
35. Raftery, A.E., and Lewis, S.M. 1992. How many iterations in the Gibbs sampler? In Bayesian Statistics 4: Proceedings of the Fourth Valencia International Meeting, April 15-20, 1991. Edited by J.M. Bernardo, J.O. Berger, P.A. David, and A.M. Smith. Clarendon Press, Oxford, pp. 763-773.
36. Raftery, A.E., Givens, G.H., and Zeh, J.E. 1995. Inference from a deterministic population dynamics model for bowhead whales. J. Am. Stat. Assoc. 90: 402-416.
37. Ripley, B.D. 1987. Stochastic simulation. John Wiley & Sons, Inc., New York.
38. Rubin, D.B. 1987. Comment on The calculation of posterior distributions by data augmentation.' J. Am. Stat. Assoc. 82: 543-546.
39. Rubin, D.B. 1988. Using the SIR algorithm to simulate posterior distributions. In Bayesian Statistics 3: Proceedings of the Third Valencia International Meeting, June 1-5, 1987. Edited by J.M. Bernardo, M.H. Degroot, D.V. Lindley, and A.M. Smith. Clarendon Press, Oxford, pp. 385-402.
40. Schnute, J.T., and Richards, L.J. 1995. The influence of error on population estimates from catch-age models. Can. J. Fish. Aquat. Sci. 52: 2063-2077.
41. Smith, A.F.M. 1991. Bayesian computational methods. Philos. Trans. R. Soc. Lond. A Math. Phys. Sci. 337: 369-386.
42. Stocker, M., Hand, C.M., and McAllister, M.K. 1994. Pacific cod. In Groundfish stock assessment for the west coast of Canada in 1993 and recommended yield options for 1994. Edited by M. Stocker. Can. Tech. Rep. Fish. Aquat. Sci. No. 1975. pp. 50-92.
43. van Dijk, H.K., and Kloek, T. 1983. Monte Carlo analysis of skew posterior distributions: an illustrative economic example. Statistician, 32: 216-233.
44. Walters, C.J., and Ludwig, D. 1994. Calculation of Bayes posterior probability distributions for key population parameters: a simplified approach. Can. J. Fish. Aquat. Sci. 51: 713-722.
45. Walters, C.J., and Punt, A.E. 1994. Placing odds on sustainable catch using virtual population analysis and survey data. Can. J. Fish. Aquat. Sci. 51: 946-958.
46. West, M. 1992. Modeling with mixtures. In Bayesian Statistics 4: Proceedings of the Fourth Valencia International Meeting, April 15-20, 1991. Edited by J.M. Bernardo, J.O. Berger, P.A. David, and A.M. Smith. Clarendon Press, Oxford, pp. 503-524.
47. West, M. 1993. Approximating posterior distributions with mixtures. J. R. Stat. Soc. B, 55: 409-422.
48. Wilderbuer, T.K., Walters, G.E., and Bakala, R.G. 1992. Yellowfin sole, Pleuronectes asper, of the eastern Bering Sea: biological characteristics, history of exploitation, and management. Mar. Fish. Rev. 54: 1-18.