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 dfm.io
An astronomer's introduction to NumPyro | Dan Foreman-Mackey
| | twiecki.io
whilemy_mcmc:gently(samples) - The Best Of Both Worlds: Hierarchical Linear Regression in PyMC
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| | [AI summary] This technical blog post explains the advantages of hierarchical Bayesian modeling over non-hierarchical approaches using a case study of predicting radon levels across different US counties with the PyMC3 library.
| | austinrochford.com
R² Priors for High-Dimensional Linear Regression and Autoregressive Timeseries in PyMC | Austin Rochford
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| | I recently read the interesting paper The ARR2 prior: flexible predictive prior definition for Bayesian auto-regressions on the arXiv and followed its references to the also fascinating Bayesian Regre
| | dsaber.com
Bayesian Regression with PyMC: A Brief Tutorial - Dan Saber
3.4 parsecs away

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| | Warning: This is a love story between a man and his Python module As I mentioned previously, one of the most powerful concepts I've really learned at Zipfian has been Bayesian inference using PyMC. PyMC is currently my favorite library of any kind in any language. I dramatically italicized "learned" because I had been taught...
| | fharrell.com
Equivalence of Wilcoxon Statistic and Proportional Odds Model - Statistical Thinking
23.4 parsecs away

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| In this article I provide much more extensive simulations showing the near perfect agreement between the odds ratio (OR) from a proportional odds (PO) model, and the Wilcoxon two-sample test statistic. The agreement is studied by degree of violation of the PO assumption and by the sample size. A refinement in the conversion formula between the OR and the Wilcoxon statistic scaled to 0-1 (corcordance probability) is provided.