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Bayesian methods for analysing ringing data

Author

Listed:
  • S. P. Brooks
  • E. A. Catchpole
  • B. J. T. Morgan
  • M. P. Harris

Abstract

A major recent development in statistics has been the use of fast computational methods of Markov chain Monte Carlo. These procedures allow Bayesian methods to be used in quite complex modelling situations. In this paper, we shall use a range of real data examples involving lapwings, shags, teal, dippers, and herring gulls, to illustrate the power and range of Bayesian techniques. The topics include: prior sensitivity; the use of reversible-jump MCMC for constructing model probabilities and comparing models, with particular reference to models with random effects; model-averaging; and the construction of Bayesian measures of goodness-of-fit. Throughout, there will be discussion of the practical aspects of the work - for instance explaining when and when not to use the BUGS package.

Suggested Citation

  • S. P. Brooks & E. A. Catchpole & B. J. T. Morgan & M. P. Harris, 2002. "Bayesian methods for analysing ringing data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(1-4), pages 187-206.
    • Handle: RePEc:taf:japsta:v:29:y:2002:i:1-4:p:187-206
    DOI: 10.1080/02664760120108683
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    References listed on IDEAS

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    1. E. A. Catchpole & B. J. T. Morgan & T. N. Coulson & S. N. Freeman & S. D. Albon, 2000. "Factors influencing Soay sheep survival," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(4), pages 453-472.
    2. Chris Chatfield, 1995. "Model Uncertainty, Data Mining and Statistical Inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(3), pages 419-444, May.
    3. A. Racine & A. P. Grieve & H. Flühler & A. F. M. Smith, 1986. "Bayesian Methods in Practice: Experiences in the Pharmaceutical Industry," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 35(2), pages 93-120, June.
    4. S. P. Brooks & E. A. Catchpole & B. J. T. Morgan & S. C. Barry, 2000. "On the Bayesian Analysis of Ring-Recovery Data," Biometrics, The International Biometric Society, vol. 56(3), pages 951-956, September.
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    Cited by:

    1. R. King & S. P. Brooks, 2002. "Model Selection for Integrated Recovery/Recapture Data," Biometrics, The International Biometric Society, vol. 58(4), pages 841-851, December.
    2. Devin S. Johnson & Jennifer A. Hoeting, 2003. "Autoregressive Models for Capture-Recapture Data: A Bayesian Approach," Biometrics, The International Biometric Society, vol. 59(2), pages 341-350, June.
    3. C. Jessica E. Metcalf & David A. Stephens & Mark Rees & Svata M. Louda & Kathleen H. Keeler, 2009. "Using Bayesian inference to understand the allocation of resources between sexual and asexual reproduction," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(2), pages 143-170, May.
    4. R. King & S. P. Brooks & B. J. T. Morgan & T. Coulson, 2006. "Factors Influencing Soay Sheep Survival: A Bayesian Analysis," Biometrics, The International Biometric Society, vol. 62(1), pages 211-220, March.
    5. S. C. Barry & S. P. Brooks & E. A. Catchpole & B. J. T. Morgan, 2003. "The Analysis of Ring-Recovery Data Using Random Effects," Biometrics, The International Biometric Society, vol. 59(1), pages 54-65, March.

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