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February 2014 Experiences in Bayesian Inference in Baltic Salmon Management
Sakari Kuikka, Jarno Vanhatalo, Henni Pulkkinen, Samu Mäntyniemi, Jukka Corander
Statist. Sci. 29(1): 42-49 (February 2014). DOI: 10.1214/13-STS431

Abstract

We review a success story regarding Bayesian inference in fisheries management in the Baltic Sea. The management of salmon fisheries is currently based on the results of a complex Bayesian population dynamic model, and managers and stakeholders use the probabilities in their discussions. We also discuss the technical and human challenges in using Bayesian modeling to give practical advice to the public and to government officials and suggest future areas in which it can be applied. In particular, large databases in fisheries science offer flexible ways to use hierarchical models to learn the population dynamics parameters for those by-catch species that do not have similar large stock-specific data sets like those that exist for many target species. This information is required if we are to understand the future ecosystem risks of fisheries.

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Sakari Kuikka. Jarno Vanhatalo. Henni Pulkkinen. Samu Mäntyniemi. Jukka Corander. "Experiences in Bayesian Inference in Baltic Salmon Management." Statist. Sci. 29 (1) 42 - 49, February 2014. https://doi.org/10.1214/13-STS431

Information

Published: February 2014
First available in Project Euclid: 9 May 2014

zbMATH: 1332.62422
MathSciNet: MR3201845
Digital Object Identifier: 10.1214/13-STS431

Rights: Copyright © 2014 Institute of Mathematical Statistics

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Vol.29 • No. 1 • February 2014
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