(I’m frustrated with the length this post and how much time it’s taking me to finish, so I’m splitting it into two parts.)

I subscribe to a school of thought some call “Jaynesian” after Edwin T. Jaynes. Its foundation is a theorem of Richard T. Cox, a physicist who studied electric eels, not to be confused with the eminent statistician Sir David R. Cox. Since my first project will be to engage with Professor Mayo’s  diametrically opposed views on the proper way to use (and think about the use of) statistics in science, it seems worthwhile to describe the theorem and the reasons I take it to be foundational to statistics — of the Bayesian variety, at least.

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  1. Cox-Jaynes foundations. In which I establish my Bayesian bona fides.
  2. Mayo’s error statistics and the Severity Principle. In which I give my current understanding of error statistics. Also, first howler!
  3. Howler, howler, howler. In which I show how the severity concept defeats many “howlers”:  common criticisms of frequentist approaches propagated in Bayesian articles and textbooks. Probably more than one post.
  4. Two severities. In which I discuss points of contact between the severity approach and the Bayesian approach, and specify a simple model in which the two approaches, operating on the exact same information, must disagree.
  5. Increasing the magnification. In which I analyze the model of the previous post, subjecting it to the most extreme conditions so as to magnify the differences between the severity approach and  the Bayesian approach. As of the time of the writing of this blogging agenda, I have not done so.  I do not know which approach, if either, will fail under high magnification. This is to be a  true test of my Bayesianism.

Not every post will contribute to the completion of above agenda; I also plan to write posts sharing my thoughts on any interesting statistical topic I happen to run into. Posts appear below in reverse chronological order; scroll to the bottom for the first one.

In the Dark

A blog about the Universe, and all that surrounds it

Minds aren't magic

Paul Crowley

Mad (Data) Scientist

Musings, useful code etc. on R and data science

djmarsay

Reasoning about reasoning, mathematically.

The Accidental Statistician

Occasional ramblings on statistics

Slate Star Codex

"Talks a good game about freedom when out of power, but once he’s in - bam! Everyone's enslaved in the human-flourishing mines."

Models Of Reality

Stochastic musings of a biostatistician.

Data Colada

Thinking about evidence and vice versa

Hacked By Gl0w!Ng - F!R3

Stochastic musings of a biostatistician.

John D. Cook

Stochastic musings of a biostatistician.

Simply Statistics

Stochastic musings of a biostatistician.

Less Wrong

Stochastic musings of a biostatistician.

Normal Deviate

Thoughts on Statistics and Machine Learning

Xi'an's Og

an attempt at bloggin, nothing more...