Where Do Priors Come From? Applying Guidelines to Construct Informative Priors in Small Sample Research Article Swipe

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Prior probability Prior information Bayesian probability Posterior probability Computer science Construct (python library) Sample (material) Estimation Statistics Econometrics Artificial intelligence Mathematics Engineering Programming language Chromatography Systems engineering Chemistry

Mariëlle Zondervan‐Zwijnenburg , Margot Peeters , Sarah Depaoli , Rens van de Schoot ·

YOU? · · 2017 · Open Access · · DOI: https://doi.org/10.1080/15427609.2017.1370966 · OA: W2767026009

This article demonstrates the usefulness of Bayesian estimation with small samples. In Bayesian estimation, prior information can be included, which increases the precision of the posterior distribution. The posterior distribution reflects likely parameter values given the current state of knowledge. An issue that has received little attention, however, is the acquisition of prior information. This study provides general guidelines to collect prior knowledge and formalize it in prior distributions. Moreover, this study demonstrates with an empirical application how prior knowledge can be acquired systematically. The article closes with a discussion that also warns against the misuse of prior information.

Where Do Priors Come From? Applying Guidelines to Construct Informative Priors in Small Sample Research Article Swipe

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