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You're right, but a) you have comp efficiency issues with MCMC, and b) just empirically MCMC models don't work as well as gradient descent + NN for many tasks.

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And you don't have computational efficiency issues with NNs?

We're also ignoring the benefits of a posterior distribution, which is useful for understanding the data-generating process.

Yeah of course. I can't explain to you why NNs outperform bayesian approaches, probably just NNs are capturing the correct type of prior for vision/language tasks. And yeah bayesian models are more interpretable but when you have millions of latent variables I'm not sure interpretability is a thing.

Yep, we arrived at my larger point: if you care about interpretability, NNs are horrible and Bayesian techniques are pretty damn great.

Well certainly, but interpretability is obsolete.

Now you're just trolling. :)

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