Inferring probabilities with a Beta prior, a third example of Bayesian calculations

Dec 11, 2014

In this post I will expand on a previous example of inferring probabilities from a data series. In particular, instead of considering a discrete set of candidate probabilities, I'll consider all (continuous) values between \( 0 \) and \( 1 \). This means our prior (and posterior) will now be a probability density function (pdf) instead of a probabilty mass function (pmf). More specifically, I'll use the Beta Distribution for this example.