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- W4307408772 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." @default.
- W4307408772 created "2022-11-01" @default.
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- W4307408772 date "2022-12-01" @default.
- W4307408772 modified "2023-09-30" @default.
- W4307408772 title "Supporting Bayesian Modeling With Visualizations" @default.
- W4307408772 doi "https://doi.org/10.52041/iase.icots11.t6c2" @default.
- W4307408772 hasPublicationYear "2022" @default.
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