Bayes Factors for Stan Models without Tears

For Christian Robert’s blog post about the bridgesampling package, click here. Bayesian inference is conceptually straightforward: we start with prior uncertainty and then use Bayes’ rule to learn from data and update our beliefs. The result of this learning process is known as posterior uncertainty. Quantities of interest can be parameters (e.g., effect size) within a single statistical model or…

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An Interactive App for Designing Informative Experiments

Bayesian inference offers the pragmatic researcher a series of perks (Wagenmakers, Morey, & Lee, 2016). For instance, Bayesian hypothesis tests can quantify support in favor of a null hypothesis, and they allow researchers to track evidence as data accumulate (e.g., Rouder, 2014). However, Bayesian inference also confronts researchers with new challenges, for instance concerning the planning of experiments. Within the…

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