Bayesian power analysis: Part III.b. What about 0/1 data?
Binary data are a little weird. In this post, we’ll focus on how to perform power simulations when using the binomial likelihood to model binary counts.
Bayesian power analysis: Part III.a. Counts are special.
Data analysts need more than the Gauss. In this post, we’ll focus on how to perform power simulations when using the Poisson likelihood to model counts.
Bayesian power analysis: Part II. Some might prefer precision to power
When researchers decide on a sample size for an upcoming project, there are more things to consider than null-hypothesis-oriented power. Bayesian researchers might like to frame their concerns in terms of precision. Stick around to learn what and how.
Bayesian power analysis: Part I. Prepare to reject \(H_0\) with simulation.
If you’d like to learn how to do Bayesian power calculations using brms, stick around for this multi-part blog series. Here with part I, we’ll set the foundation.
Would you like all your posteriors in one plot?
In response to a DM question, here we practice a few different ways you can combine the posterior samples from your Bayesian models into a single plot.
Stein’s Paradox and What Partial Pooling Can Do For You
In many instances, partial pooling leads to better estimates than taking simple averages will, a finding sometimes called Stein’s Paradox. In 1977, Efron and Morris published a great paper discussing the phenomenon. In this post, I’ll walk out Efron and Morris’s baseball example and then link it to contemporary Bayesian multilevel models.
Bayesian Correlations: Let’s Talk Options.
There’s more than one way to fit a Bayesian correlation in brms. Here we explore a few.
Bayesian robust correlations with brms (and why you should love Student’s \(t\))
In this post, we’ll show how Student’s t-distribution can produce better correlation estimates when your data have outliers. As is often the case, we’ll do so as Bayesians.
Robust Linear Regression with Student’s \(t\)-Distribution
The purpose of this post is to demonstrate the advantages of the Student’s t-distribution for regression with outliers, particularly within a Bayesian framework.