I agree Bayesian approaches to multilevel modeling situations are clearly quite useful and popular.
Ironically this has been one of the primary examples of, in my personal experience, with the problems I have worked on, frequentist mixed & random effects models have worked just fine. On rare occasions I have encountered a situation where the data was particularly complex or I wanted to use an unusual compound probability distribution and thought Bayesian approaches would save me. Instead, I have routinely ended up with models that never converge or take unpractical amounts of time to run. Maybe it’s my lack of experience jumping into Bayesian methods only on super hard problems. That’s totally possible.
But I have found many frequentist approaches to multilevel modeling perfectly adequate. That does not, of course, mean that will hold true for everyone or all problems.
One of my hot takes is that people seriously underestimate the diversity of data problems such that many people can just have totally different experiences with methods depending on the problems they work on.
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 because when I do turn to them it tends to be in situations that already quite complex and large. In basically every instance of that I’ve never been able to make the Bayesian approach work. Won’t converge or the sampler says it will take days and days to run. I can almost always just resort to some resampling method that might take a few hours but it runs and gives me sensible results.
I realize this is heavily biased by basically only attempting on super-complex problems, but it has sort of soured me on even trying anymore.
To be clear I have no issue with Bayesian methods. Clearly they work well and many people use them with great success. But I just haven’t encountered anything in several decades of statistical work that I found really required Bayesian approaches, so I’ve really lost any motivation I had to experiment with it more.
Ironically this has been one of the primary examples of, in my personal experience, with the problems I have worked on, frequentist mixed & random effects models have worked just fine. On rare occasions I have encountered a situation where the data was particularly complex or I wanted to use an unusual compound probability distribution and thought Bayesian approaches would save me. Instead, I have routinely ended up with models that never converge or take unpractical amounts of time to run. Maybe it’s my lack of experience jumping into Bayesian methods only on super hard problems. That’s totally possible.
But I have found many frequentist approaches to multilevel modeling perfectly adequate. That does not, of course, mean that will hold true for everyone or all problems.
One of my hot takes is that people seriously underestimate the diversity of data problems such that many people can just have totally different experiences with methods depending on the problems they work on.