https://medium.com/@keith-mcnulty/a-lovely-introdu...

I found the article. It’s “A Lovely Introduction To Bayesian Statistics” by Keith McNulty, a Medium member-only story. The visible portion says it uses a Cambridge STEP probability problem about trousers and ties to illustrate Bayes’ Theorem. (Medium) The official STEP page identifies the problem as STEP II 1990 Question 16, tagged as “Trousers, ties and compromising photographs, elementary probability.” (step.maths.org)

The core Bayesian idea is:

P(H\mid E)=\frac{P(E\mid H)P(H)}{P(E)}

In plain English: Bayesian statistics lets you update the probability of a possible explanation after seeing evidence. In this article’s setup, the “evidence” is likely an observed clothing item, and the “hypothesis” is something like which trousers were worn. The trick is to carefully write down the conditional probabilities, then reverse the direction of the question using Bayes’ Theorem.

The article’s visible lesson is a good one: before trying to solve a probability problem, write down what you know in a structured table. McNulty says the problem looks complex at first, but the method emerges once the information is organized. (Medium)

I can only see the public excerpt, not the full member-only text. Paste the full article text here and I’ll give you a complete summary or walk through the math step by step.