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The exact opposite is true. Virtually everyone’s intuition is aligned with the Bayesian model. That intuition has to be hammered out of people in their stats classes because for decades frequentist approaches were computationally more feasible, even if they don’t align with how most humans interpret probability.
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I feel like I'm a polyglot here but primarily a native frequentist thinker. I've found Bayesian methods shine in cases of an "intractible partition function". Cases such as language models, where the cardinality of your discrete probability distribution is extremely large, to the point of intractability. Bayesians tend to immediately go to things like Monte Carlo estimation. Is that fundamentally Bayesian and anti-fr…
When you are using something like Monte Carlo you’re probably using some method that’s more advanced than the Naïve Bayes, is that right?
Or the temperature setting for an LLM etc.
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Huh? Are there really any pure frequentists post Stein's paradox? At least ones that are aware of it and maintain objections to fusing the fields?
> Are there really any pure frequentists post Stein's paradox? What does that have to do with anything? If one cares about that using a shrinkage estimator is an option which maintains the frequentist purity.
Purist frequentists using a shrinkage estimator looks a lot like heliocentric Ptolemic astronomy.
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Huh? Are there really any pure frequentists post Stein's paradox? At least ones that are aware of it and maintain objections to fusing the fields?
Downvote me all you want. Bayesianism is misapplied much more frequently than frequentism. It just makes it way too easy to fudge p values. Sorry not sorry.
Just saying the word "Bayesian" doesn't actually make it different
I went through grad school in a very frequentist environment. We “learned” Bayesian methods but we never used them much. In my professional life I’ve never personally worked on a problem that I felt wasn’t adequately approached with frequentist methods. I’m sure other people’s experiences are different depending on the problems you gravitate towards. In fact, I tend to get pretty frustrated with Bayesian approaches b…
> I’ve never personally worked on a problem that I felt wasn’t adequately approached with frequentist methods Multilevel models are one example of problem were Bayesian methods are hard to avoid as otherwise inference is unstable, particularly when available observations are not abundant. Multilevel models should be used more often as shrinking of effect sizes is important to make robust estimates. Lots of flashy res…
Horshoe priors, partial pooling, something more?
I realize that might be highly subject
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> I’ve never personally worked on a problem that I felt wasn’t adequately approached with frequentist methods Multilevel models are one example of problem were Bayesian methods are hard to avoid as otherwise inference is unstable, particularly when available observations are not abundant. Multilevel models should be used more often as shrinking of effect sizes is important to make robust estimates. Lots of flashy res…
Curious what you might consider “adequate shrinking”? Horshoe priors, partial pooling, something more? I realize that might be highly subject
But I was thinking about a typical hierarchical model with partial pooling and standard weakly informative priors.
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Downvote me all you want. Bayesianism is misapplied much more frequently than frequentism. It just makes it way too easy to fudge p values. Sorry not sorry.
I do always laugh when I see a Bayesian object to p-values, then use a Bayesian procedure that is mathematically identical to treating p values as posterior probabilities. Just saying the word "Bayesian" doesn't actually make it different
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I do always laugh when I see a Bayesian object to p-values, then use a Bayesian procedure that is mathematically identical to treating p values as posterior probabilities. Just saying the word "Bayesian" doesn't actually make it different
It’s mathematically identical but conceptually different. The things that go into the calculation are different, the numbers that get out of the calculation mean different things. Laughing is healthy though.
And if you're doing that, don't care complain that p-values can be misinterpreted, because you're basically just laundering the misinterpretation of p-values.
Sure, you are mathematically pure because you made an initial assumption that it can be so, rather than being confused, but the end result is the same.
I went through grad school in a very frequentist environment. We “learned” Bayesian methods but we never used them much. In my professional life I’ve never personally worked on a problem that I felt wasn’t adequately approached with frequentist methods. I’m sure other people’s experiences are different depending on the problems you gravitate towards. In fact, I tend to get pretty frustrated with Bayesian approaches b…
Bayesian approaches take a long time thinking, making models, choosing priors, simulations etc. but they provide a better estimate and understanding the parameters. I hate point estimates and decision based arbitrary p-value. Whenever possible I use Bayesian methods.