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I was born at the end of the cold war, unaware of the danger we were emerging from. Duck and cover drills (as if those could protect schoolchildren from a nuclear blast) were a curiosity of the past.
The farmer-poet Wendell Berry, who wrote about the dread of nuclear war, died recently. From his 1968 “The peace of wild things”:
When despair for the world grows in me
and I wake in the night at the least...
I think most frequentists would agree that Bayesian inference is more intuitive. Bayesian inference is much more computationally difficult though, and you usually get the same answer anyways. (Bayesian estimators are typically asymptotically equivalent to classical estimators!)
> and you usually get the same answer anyways
I don't agree with this! In reality we don't get asymptotic properties, we get finite sample properties, and these can vary greatly. E.g. MLE often won't even converge for hierarchical models without a fair amount of data. Also, for bespoke models there often isn't a published frequentist estimator available, and attempting to derive one would be a much bigger issue for most people than the computational resources required for MCMC or variational inference.