Bayesian ideas revisited
In the last chapter, we talked, very briefly, about Bayesian ways of thinking. In short, when speaking about Bayes, you are speaking about the following three things and how they all interact with each other:
- A prior distribution
- A posterior distribution
- A likelihood
Basically, we are concerned with finding the posterior. That's the thing we want to know.
Another way to phrase the Bayesian way of thinking is that data shapes and updates our belief. We have a prior probability, or what we naively think about a hypothesis, and then we have a posterior probability, which is what we think about a hypothesis, given some data.
Bayes theorem
Bayes theorem is the big result of Bayesian inference. Let's see how it even comes about. Recall ...
Get Principles of Data Science now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
