Year: 2022
Type: conference-paper
Abstract: With computational advances, Bayesian modeling is becoming more accessible. But because Bayesian thinking often differs from learners’ previous statistics training, it can be challenging for novice Bayesian learners to conceptualize and interpret the three major components of a Bayesian analysis: the prior, likelihood, and posterior. To this end, we developed an R package, bayesrules, which provides tools for exploring common introductory Bayesian models: beta-binomial, gamma-Poisson, and normal-normal. Specifically, within these model settings, the bayesrules functions provide an active learning opportunity to interact with the three Bayesian model components, as well as the effects of different model settings on the model results. We present here the package’s visualization functions and how they can be utilized in a statistics classroom.
Authors: Mine Dogucu, Alicia Johnson
Institutions: University College London, Macalester College
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
SDG: Quality education
Open Access status: bronze
OpenAlex ID: W4307408772