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Supporting Bayesian Modeling With Visualizations

Dogucu, Mine; Johnson, Alicia; (2022) Supporting Bayesian Modeling With Visualizations. In: Proceedings of the 11th International Conference on Teaching Statistics (ICOTS11 2022). International Association for Statistical Education: Rosario, Argentina. Green open access

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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.

Type: Proceedings paper
Title: Supporting Bayesian Modeling With Visualizations
Event: Bridging the Gap: Empowering and Educating Today’s Learners in Statistics
Open access status: An open access version is available from UCL Discovery
DOI: 10.52041/iase.icots11.t6c2
Publisher version: https://doi.org/10.52041/iase.icots11.T6C2
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10162720
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