The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Non-Linear State Space Modelling of Fisheries Biomass Dynamics by Using Metropolis-Hastings within-Gibbs Sampling
Abstract
SUMMARY: State space modelling and Bayesian analysis are both active areas of applied research in fisheries stock assessment. Combining these two methodologies facilitates the fitting of state space models that may be non-linear and have non-normal errors, and hence it is particularly useful for modelling fisheries dynamics. Here, this approach is demonstrated by fitting a non-linear surplus production model to data on South Atlantic albacore tuna (Thunnus alalunga). The state space approach allows for random variability in both the data (the measurement of relative biomass) and in annual biomass dynamics of the tuna stock. Sampling from the joint posterior distribution of the unobservables was achieved by using Metropolis-Hastings within-Gibbs sampling.
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Cites
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
Sampling-based approaches to calculating marginal densities
Gelfand, A. E., & Smith, A. F. M. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10476213 · Cited by 6,606
Bayesian approaches to randomized trials
Spiegelhalter, D. J., Freedman, L. S., & Parmar, M. K. B. (1994) · Journal of the Royal Statistical Society. Series A (Statistics in Society) · doi:10.2307/2983527 · Cited by 720
Cited by
Maximum reproductive rate of fish at low population sizes
Myers, R. A., Bowen, K. G., & Barrowman, N. J. (1999) · Canadian Journal of Fisheries and Aquatic Sciences · doi:10.1139/f99-201 · Cited by 503
Remedies for pseudoreplication
Millar, R. B., & Anderson, M. J. (2004) · Fisheries Research · doi:10.1016/j.fishres.2004.08.016 · Cited by 292
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Metadata
- Authors
- Millar, R. B., & Meyer, R.
- Venue
- Journal of the Royal Statistical Society Series C: Applied Statistics
- Publisher
- Oxford University Press (OUP)
- Published
- 2000-09-01
- Volume
- 49
- Issue
- 3
- Pages
- 327-342
- ISSN
- 0035-9254 (ISSN-L)
- 1467-9876
- OpenAlex
- W2166378513
- Cited by
- 159
- References
- 3
- FWCI
- 2.83
- Type
- journal article
- Language
- en
- Open access
- closed
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- Millar, R. B., & Meyer, R. (2000). Non-Linear State Space Modelling of Fisheries Biomass Dynamics by Using Metropolis-Hastings within-Gibbs Sampling. Journal of the Royal Statistical Society Series C: Applied Statistics. https://doi.org/10.1111/1467-9876.00195
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