even the Bayesian interpretation of probability theory has only been accepted for… well, I’m not sure, but I think only since World War II
Try half a century later. Until very recently—about twenty years ago—the Bayesian view of probability was very much a minority view, and it has only really picked up steam in the last 10 years. Several things happened around 20 years ago:
Faster and cheaper computers became available. Bayesian methods tend to be computationally intensive, and this limited their use.
Rule-based expert systems fizzled out and began to be replaced by Bayesian networks after practical algorithms for inference with BNs were developed.
Awareness of Markov chain Monte Carlo methods (which can be used to sample from a Bayesian posterior distribution) spread to the statistics community, and the free BUGS software made it easy for non-experts to create and evaluate new Bayesian models.
These developments made it practical to apply Bayesian methods… and people started finding out how well they could work.
Try half a century later. Until very recently—about twenty years ago—the Bayesian view of probability was very much a minority view, and it has only really picked up steam in the last 10 years. Several things happened around 20 years ago:
Faster and cheaper computers became available. Bayesian methods tend to be computationally intensive, and this limited their use.
Rule-based expert systems fizzled out and began to be replaced by Bayesian networks after practical algorithms for inference with BNs were developed.
Awareness of Markov chain Monte Carlo methods (which can be used to sample from a Bayesian posterior distribution) spread to the statistics community, and the free BUGS software made it easy for non-experts to create and evaluate new Bayesian models.
These developments made it practical to apply Bayesian methods… and people started finding out how well they could work.