Bayesian Analysis with Python

Single parameter inference

In the last two sections, we have learned several important concepts, but two of them are essentially the core of Bayesian statistics, so let's restate them in a single sentence. Probabilities are used to measure the uncertainty we have about parameters, and Bayes' theorem is the mechanism to correctly update those probabilities in the light of new data, hopefully reducing our uncertainty.

Now that we know what Bayesian statistics is, let's learn how to do Bayesian statistics with a simple example. We are going to begin inferring a single unknown parameter.

The coin-flipping problem

The coin-flip problem is a classical problem in statistics and goes like this. We toss a coin a number of times and record how many heads and ...