Frequentism and Bayesianism: A Python-driven Primer

TL;DR

This paper compares frequentist and Bayesian statistical inference, highlighting their philosophical differences, illustrating their methods with Python examples, and reviewing relevant Python packages for each approach.

Contribution

It provides a semi-technical, example-driven comparison of frequentist and Bayesian methods, including practical implementation insights with Python packages.

Findings

Highlights fundamental philosophical differences between approaches

Demonstrates implementation of methods in Python

Reviews leading Python packages for statistical inference

Abstract

This paper presents a brief, semi-technical comparison of the essential features of the frequentist and Bayesian approaches to statistical inference, with several illustrative examples implemented in Python. The differences between frequentism and Bayesianism fundamentally stem from differing definitions of probability, a philosophical divide which leads to distinct approaches to the solution of statistical problems as well as contrasting ways of asking and answering questions about unknown parameters. After an example-driven discussion of these differences, we briefly compare several leading Python statistical packages which implement frequentist inference using classical methods and Bayesian inference using Markov Chain Monte Carlo.