Bayesian Inference for Fisheries Scientists

An Introduction to Bayesian Inference for Fisheries Scientists

Jason C. Doll and Stephen J. Jacquemin

Submitted to Fisheries Magazine

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 familiarity with null hypotheses, t-tests, linear regression, and ANOVA. To this end, we review the general process of Bayesian inference without the use of complicated equations; discuss a simple teaching example; 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 with new data; and finally, suggest readings that can help to develop the skills needed to use Bayesian inference in your own management or research program.