NOTE: This post is an parody in the same genre as How to Burn Money and Computing Power for a Simple Median. This post was generated using the same code as in the previous post, but with the prompt: Write a post in the style of Towards Data Science about the following code. Keep in mind that this code is showing how to estimate an unconditional median using a deep learning model.

NOTE: This is a parody post. It was generated by providing my code to GPT-4 with the prompt: Turn this into a blog post with the title "World's Most Expensive Way to Compute a Mean or Median" and then asking GPT-4 to make its initial post even more entertaining and sarcastic. Introduction Today, we dive into the comedic world of absurdly over-engineered solutions for simple problems. Our target? Calculating a mean or median using R and TensorFlow in what might be the most hilariously unnecessary method ever conceived.

Preamble 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 some connections to confidence distributions and fiducial inference. But without any of the details for this to make sense for a newcomer. For the former material, you are better served by page 73 of A First Course in Bayesian Statistical Methods by Peter Hoff or page 67 of Bayesian Data Analysis.