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Sampling variability and estimates of den- Gilks, W. Ecology eds. Markov Chain Monte Carlo in Practice. Chap- 87 — MR Leslie, P. On the use of matrices in certain pop- Gilks, W. Strategies for im- ulation mathematics. Biometrika 33 — Some further notes on the use of ma- W. Gilks, S. Richardson and D. Spiegelhalter, eds. Biometrika 35 — Chapman and Hall, London. MR Gordon, N.

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Liu, J. MR Bayesian state estimation. IEE Proceedings-F — Sequential Monte Carlo Green, P. Reversible jump Markov chain Monte methods for dynamic systems. MR Biometrika 82 — MR McAllister, M. Bayesian Gudmundsson, G. Time series models of fishing stock assessment using catch-age data and the sampling- mortality rates. Report RH, Raunvisindastofnun importance sampling algorithm. Fisheries and Haskolans, Univ. Aquatic Sciences 54 — Gudmundsson, G. Time series analysis of catch-at- McAllister, M. Hilborn, R. A Bayesian approach to stock assess- Gurney, W. Oxford Univ. Press, New York.

Fisheries and Aquatic Harvey, A. Cambridge Univ. McConnell, B. Coping with uncer- mond, P.

Modelling Population Dynamics

Movements and foraging areas of grey tainty in ecological advice: Lessons from fisheries. Trends seals in the North Sea. Applied Ecology 36 — Mendelssohn, R. Some problems in estimating pop- Hilborn, R. Fishery Bulletin 86 A Bayesian estimation and decision analysis for an — Fisheries Meyer, R. Bayesian stock as- Research 19 17— Fisheries and Aquatic Sci- Schnute, J. A general framework for develop- ences 56 37— Fisheries and Millar, R. Non-linear state- Aquatic Sciences 51 — A Kalman filter approach to catch-at- Metropolis—Hastings within-Gibbs sampling.


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Biometrics 48 — MR Thomas, L. Classical and Modern Regression with Harwood, J. A unified framework for modelling Applications, 2nd ed.

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PWS-Kent, Boston. Newman, K. State-space modeling of animal MR Thompson, D. Bio- Fedak, M. Movements, diving and foraging be- metrics 54 — Zoology Newman, K. Hierarchic modeling of salmon har- — MR T. Calibrating population dynamics models to Newman, K.

Modelling In Biology Using Population Dynamics

Thomas, L. Hidden process J. Ecological Appli- Tuljapurkar, S.

Model Formulation, Fitting and Assessment using State-Space Methods

Stochastic matrix models. In cations 16 74— Tuljapurkar and H. Caswell, eds. Monte Carlo inference for state-space 59— Walters, C. Filtering via simula- sources. Blackburn, Caldwell, NJ.

Modelling Population Dynamics : K. B. Newman (author), : : Blackwell's

Approximating posterior distributions — MR by mixtures. B 55 — Poole, D. Mixture models, Monte Carlo, Bayesian proach. MR updating and dynamic models. In Computing Science and Quinn, T.


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  • II and Deriso, R. Quantitative Fish Statistics: Proc. Raftery, A. West, M. Bayesian Forecast- Inference from a deterministic population dynamics model ing and Dynamic Models, 2nd ed. MR Assoc. Wikle, C. Hierarchical Bayesian models for pre- Rivot, E. Ecology 84 — A Bayesian state-space modelling framework for fit- Ecological Modelling Hierarchical Bayesian space—time models.

    http://jordants.org/components/classic/soil-sampling-and-methods-of-analysis-second-edition.php Environmental — Rubin, D. In Bayesian Statistics 3 J.