Practically Insignificant

Author

David Darmon

Estimating the Unconditional Median with Deep Learning: A Practical Approach Using R and Keras

deep learning

NOTE: This post is an parody in the same genre as How to Burn Money and Computing Power for a Simple Median.

GPT-4, with Assistance from David Darmon

How to Burn Money and Computing Power for a Simple Median

deep learning

NOTE: This is a parody post. It was generated by providing my code to GPT-4 with the prompt:

GPT-4, with Assistance from David Darmon

Inference via Stan for the Mean and Variance of a Gaussian (“Normal”) Population with Weakly Informative and Fiducial Priors

statistics

Bayesian statistics

fiducial inference

Attenion Conservation Notice: I implement the now-standard Bayesian procedure for estimating a Gaussian mean and variance with weakly informative priors using Stan and make…

Bounded Bayes: Markov Chain Monte Carlo (MCMC) for Posteriors of Bounded Parameters

statistics

Bayesian statistics

This is largely a note to my past-self on how to easily use Markov Chain Monte Carlo (MCMC) methods for Bayesian inference when the parameter you are interested in has…

How Confident Are We that Masks “Work”? Confidence Functions and the DANMASK-19 Study

statistics

confidence functions

Attention Conservation Notice: I use a recent study about COVID-19 as an excuse to demo an R package I am developing. Some other reasons not to read this: (a) I am not an…

Salvaging Lost Significance via Randomization: Randomized (P)-values for Discrete Test Statistics

statistics

Last time, we saw that when performing a hypothesis test with a discrete test statistic, we will typically lose size unless we happen to be very lucky and have the…

Quantum Statistics: Exact Tests with Discrete Test Statistics

statistics

For those who ended up here looking for information about quantum statistical mechanics or particle statistics: apologies! But sometimes I feel like physicists took all the…