Frequentism and Bayesianism: A Python-driven Primer
Jake Vanderplas
- pp 85-93
TLDR
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.Abstract:
This paper presents a brief, semi-technical comparison of the es- sential features of the frequentist and Bayesian approaches to statistical infer- ence, with several illustrative examples implemented in Python. The differences between frequentism and Bayesianism fundamentally stem from differing defini- tions 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 sta- tistical packages which implement frequentist inference using classical methods and Bayesian inference using Markov Chain Monte Carlo. 1read more
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