Most of the stats we are exposed to in our formative years as statisticians are viewed through a frequentist lens. Bayesian methods are often viewed with scepticism, perhaps due in part to a lack of understanding over how to specify our prior distribution and perhaps due to uncertainty as to what we should do with the posterior once we’ve got it.
Below is a copy of a Jupyter Notebook where we walk through an example of binary data in a Bayesian setting. We explain how to elicit a prior and discuss how it can affect our analysis. Hopefully this post will be useful for those who are considering using Bayesian methods in their own work or trying to understand them a little better.
