
Bayesian information theory
April 9, 2021
Shannon’s information theory defines quantity of information (e.g. selfinformation $\lg p(x)$) in terms of probabilities. In the context of data compression, these probabilities are given a frequentist interpretation (Shannon makes this interpretation explicit in his 1948 paper). In Deconstructing Bayesian Inference, I introduced the idea of a subjective data distribution. If quantities of information are calculated using a subjective data distribution, what is their meaning? Below I will answer this question by building, from the groundup, a different notion of Bayesian inference. …

Deconstructing Bayesian Inference
March 31, 2021
I pose the question: Why predict probabilities rather than predicting outcomes without probabilities? I first define Bayesian inference, and then I remove the probabilities involved in multiple passes until there is no probability. Then I examine what the result is, and eventually motivate bringing probabilities back into our predictions. …

Classical vs Bayesian Reasoning
February 24, 2021
My goal is to identify the core conceptual difference between someone who accepts “Bayesian reasoning” as a valid way to obtain knowledge about the world, vs someone who does not accept Bayesian reasoning, but does accept “classical reasoning”. By classical reasoning, I am referring to the various forms of logic that have been developed, starting with Aristotelian logic, through propositional logic like that of Frege, and culminating in formal mathematics (e.g. higher order type theory). In such logics, the goal is to uniquely determine the truth values of things (such as theorems and propositions) from givens. …