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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: 0s | 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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RandomThoughts3 ◴[26 Jul 24 18:28 UTC] No.41080979[source]▶
I’m always puzzled by this because while I come from a country where the frequentist approach generally dominates, the fight with Bayesian basically doesn’t exist. That’s just a bunch of mathematical theories and tools. Just use what’s useful.

I’m still convinced that Americans tend to dislike the frequentist view because it requires a stronger background in mathematics.

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1. thegginthesky ◴[26 Jul 24 19:03 UTC] No.41081297[source]▶
It's because practicioners of one says that the other camp is wrong and question each other's methodologies. And in academia, questioning one's methodology is akin to saying one is dumb.

To understand both camps I summarize like this.

Frequentist statistics has very sound theory but is misapplied by using many heuristics, rule of thumbs and prepared tables. It's very easy to use any method and hack the p-value away to get statistically significant results.

Bayesian statistics has an interesting premise and inference methods, but until recently with the advancements of computing power, it was near impossible to do simulations to validate the complex distributions used, the goodness of fit and so on. And even in the current year, some bayesian statisticians don't question the priors and iterate on their research.

I recommend using methods both whenever it's convenient and fits the problem at hand.