Jake VanderPlas has been writing a series of posts discussing frequentism and bayesianism. They are well-written, clear and insightful and use IPython for the statistical analysis. Here, I compiled his posts on the topic for convenience.
Frequentism and Bayesianism: A Practical Introduction
where he synthesizes the philosophical and pragmatic aspects of the frequentist and Bayesian approaches as they relate to the analysis of scientific data.
Frequentism and Bayesianism II: When Results Differ
where he discusses the difference between frequentist and Bayesian in the treatment of nuisance parameters.
Frequentism and Bayesianism III: Confidence, Credibility, and why Frequentism and Science do not Mix
where he discusses the subtle difference between frequentist confidence intervals and Bayesian credible intervals.
Frequentism and Bayesianism IV: How to be a Bayesian in Python
where he describes how to do Bayesian statistics in python with emcee, PyMC and PyStan.
Frequentism and Bayesianism: A Practical Introduction
where he synthesizes the philosophical and pragmatic aspects of the frequentist and Bayesian approaches as they relate to the analysis of scientific data.
Frequentism and Bayesianism II: When Results Differ
where he discusses the difference between frequentist and Bayesian in the treatment of nuisance parameters.
Frequentism and Bayesianism III: Confidence, Credibility, and why Frequentism and Science do not Mix
where he discusses the subtle difference between frequentist confidence intervals and Bayesian credible intervals.
Frequentism and Bayesianism IV: How to be a Bayesian in Python
where he describes how to do Bayesian statistics in python with emcee, PyMC and PyStan.
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