Explores practical differences between Bayesian and Frequentist statistical methods using a sci-fi probability problem.
Explores the Bayesian equivalent of a two-sample t-test, questioning traditional assumptions and proposing a model using discrete distributions.
Explains why Monte Carlo simulation is essential for Bayesian hypothesis testing, using A/B testing and election forecasting as examples.
An interactive Shiny app for exploring Bayesian surprise, showing how prior and likelihood tail heaviness affect posterior beliefs.
Explores Bayesian methods for quantifying uncertainty in deep neural networks, moving beyond single-point weight estimates.
Compares frequentist and Bayesian approaches to statistical model selection, highlighting philosophical differences and computational trade-offs.
Analyzes publication bias in scientific reporting using a humorous example of socks and Bayesian statistics.
A response to a critique of the author's introductory series on Frequentist vs. Bayesian statistics, focusing on audience and the role of decision theory.
A practical guide to implementing Bayesian analysis in Python using MCMC packages like emcee, PyMC, and PyStan, with a line-fitting example.
Explores differences between frequentist and Bayesian statistics, focusing on how they handle nuisance parameters in data analysis.
Explains how Bayesian A/B testing improves online headline optimization, overcoming challenges of traditional frequentist methods for faster, more accurate results.
Compares Bayesian vs frequentist statistics for introductory courses, highlighting pedagogical pros and cons of each approach.
