Introduction to Bayesian Modeling and Inference for Fisheries Scientists

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

Authors Jason C. Doll, Stephen J. Jacquemin
Journal/Conference Name Transactions of the American Fisheries Society
Paper Category
Paper 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 undergo when selecting 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 that the reader is familiar with Bayesian inference; however, we do assume that 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 literature that can help to develop the skills needed to use Bayesian inference in your own management or research program.
Date of publication 2018
Code Programming Language R

Copyright Researcher 2022