Bayesian probability is catching on because the technology has finally caught up to the point where it's feasible. Thanks to the web and the proliferation of big data, we now have enough observations that Bayesian models can be trained (this was always the hard part). This doesn't mean we don't need smart people figuring out what things the model should and shouldn't look at; but it's at least possible to do today.
It also doesn't hurt that the Bayes theorem is at its heart a map-reduce problem. Where once Bayes' theorem was considered a cumbersome artifact for "brute forcing" probability, it's now likely faster than competing methods of statistical analysis.
We almost seem to be getting to the point in ad targeting where demographics are expressed in terms of a set of bayesian properties. You don't even care what the properties are; just that they're potentially more willing than average to use your product.
It also doesn't hurt that the Bayes theorem is at its heart a map-reduce problem. Where once Bayes' theorem was considered a cumbersome artifact for "brute forcing" probability, it's now likely faster than competing methods of statistical analysis.
We almost seem to be getting to the point in ad targeting where demographics are expressed in terms of a set of bayesian properties. You don't even care what the properties are; just that they're potentially more willing than average to use your product.