Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference

6. Getting Our Priorities Straight

6.1 Introduction

This chapter focuses on the most debated and discussed part of Bayesian methodologies: how to choose an appropriate prior distribution. We also present how the prior’s influence changes as our dataset grows larger, and an interesting relationship between priors and penalties on linear regression.

Throughout this book, we have mostly ignored our choice of priors. This is unfortunate, as we can be very expressive with our priors, but we also must be careful about choosing them. This is especially true if we want to be objective, that is, not to express any personal beliefs in the priors.

6.2 Subjective versus Objective Priors

Bayesian priors can be classified into two classes. The first are ...