MODELING STOCK DYNAMICS WITH KNOWN ESTIMATIONS FOR ABUNDANCE AND CATCH AT AGE. II
Abstract
To model time series of fish abundance and catch at age data we use an approach that is based on simple fish arithmetic and takes into account unavoidable uncertainty. We assume that measurement and estimation errors are random variables and have the Laplace or Gaussian distributions. First, we evaluate coefficients of the natural and fishing mortality and parameters of density functions. Then, we represent each cohort as a dynamic Bayesian network with abundances as hidden states and catches as observations. Parameters of distribution densities for transition and observation models are evaluated at the first stage. For each cohort, we consider how to perform such basic Bayesian estimation tasks as filtering, smoothing, and prediction. We illustrate the framework application with the case of a linear Gaussian family of distributions when all Bayesian estimations are relatively simply done. All estimations and graphing were made in Julia with use of different libraries including the Fishmetica package. All symbolic manipulations were carried out in Mathematica.
About the Author
I. I. Shevchenko
The Pacific Branch of Russian Federal Research Institute of Fisheries and Oceanography
Russian Federation
Russian Federation
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Review
For citations:
Shevchenko I.I. MODELING STOCK DYNAMICS WITH KNOWN ESTIMATIONS FOR ABUNDANCE AND CATCH AT AGE. II. Problems of Fisheries. 2019;20(2):152-163. (In Russ.)
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