https://github.com/jcdoll79/introbayes

Computer code to accompany "An Introduction to Bayesian Modeling and Inference for Fisheries Scientists
https://github.com/jcdoll79/introbayes

bayesian-inference fisheries fisheries-management growth-curve-models

Last synced: about 1 month ago
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Computer code to accompany "An Introduction to Bayesian Modeling and Inference for Fisheries Scientists

• Host: GitHub
• URL: https://github.com/jcdoll79/introbayes
• Owner: jcdoll79
• License: gpl-3.0
• Created: 2018-01-10T18:28:01.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2023-12-07T13:27:59.000Z (11 months ago)
• Last Synced: 2024-06-11T19:29:09.859Z (5 months ago)
• Topics: bayesian-inference, fisheries, fisheries-management, growth-curve-models
• Language: R
• Size: 59.6 KB
• Stars: 1
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Introduction to Bayesian modeling and inference for fisheries scientists

JAGS and Stan code to accompany:

Doll, J.C. and S.J. Jacquemin. 2018. Fisheries Magazine 43(3):152-161.

http://dx.doi.org/10.1002/fsh.10038

Abstract

Bayesian inference is everywhere, from one of the most recent journal articles in Transactions of the American Fisheries Society to the decision making process you go through when you select a new fishing spot. Bayesian inference is the only statistical paradigm that synthesizes prior knowledge with newly collected data to facilitate a more informed decision – and it is being used at an increasing rate in almost every area of our profession. Thus, the goal of this article is to provide fisheries managers, educators, and students with a conceptual introduction to Bayesian inference. We do not assume the reader is familiar with Bayesian inference, however, we do assume the reader has completed an introductory biostatistics course. To this end, we review the conceptual foundation of Bayesian inference without the use of complex equations; present one example of using Bayesian inference to compare relative weight between two time periods; present one example of using prior information about von Bertalanffy growth parameters to improve parameter estimation; and finally, suggest readings that can help to develop the skills needed to use Bayesian inference in your own management or research program.

Data used in the analysis were provided by Sandra Clark-Kolaks and Brian Breidert from the Indiana Department of Natural Resources