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Bayesian Statistics: The three cultures

(statmodeling.stat.columbia.edu)
309 points luu | 1 comments | 26 Jul 24 17:15 UTC | HN request time: 0.211s | source
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thegginthesky ◴[26 Jul 24 17:55 UTC] No.41080693[source]▶
I miss the college days where professors would argue endlessly on Bayesian vs Frequentist.

The article is very well succinct and even explains why even my Bayesian professors had different approaches to research and analysis. I never knew about the third camp, Pragmatic Bayes, but definitely is in line with a professor's research that was very through on probability fit and the many iteration to get the prior and joint PDF just right.

Andrew Gelman has a very cool talk "Andrew Gelman - Bayes, statistics, and reproducibility (Rutgers, Foundations of Probability)", which I highly recommend for many Data Scientists

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spootze ◴[26 Jul 24 18:11 UTC] No.41080841[source]▶
Regarding the frequentist vs bayesian debates, my slightly provocative take on these three cultures is

- subjective Bayes is the strawman that frequentist academics like to attack

- objective Bayes is a naive self-image that many Bayesian academics tend to possess

- pragmatic Bayes is the approach taken by practitioners that actually apply statistics to something (or in Gelman’s terms, do science)

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1. skissane ◴[27 Jul 24 00:11 UTC] No.41083494[source]▶
> - subjective Bayes is the strawman that frequentist academics like to attack

I don’t get what all the hate for subjective Bayesianism is. It seems the most philosophically defensible approach, in that all it assumes is our own subjective judgements of likelihood, the idea that we can quantify them (however in exactly), and the idea (avoid Dutch books) that we want to be consistent (most people do).

Whereas, objective Bayes is basically subjective Bayes from the viewpoint of an idealised perfectly rational agent - and “perfectly rational” seems philosophically a lot more expensive than anything subjective Bayes relies on.