Well, no, because I don't know any good reasons for using Bayesian methods (except when prior probability distributions can be found objectively through some previous experiment etc).
how do you reconcile "I don't know any good reasons for using Bayesian methods" with the fact that Bayesian methods revolutionized spam-filtering? (or maybe you disagree they did?)
Naive Bayes revolutionzed spam filtering because it is incredibly easy to implement and understand, and was reasonably effective for early spam, not because it was the best model for detecting spam. There's a reason we started seeing ads for "v1agra" and snippets of prose -- it's pretty easy to game Naive Bayes.
On the other hand, the GP's assertion that there is seldom a need for using Bayesian methods is also unwarranted; they are the basis for so many machine learning algorithms in common use -- particle filters, for example.
That's a good question and I was asking myself that I after I wrote that comment. I think my objection is more to the 'Bayesian' and less to the 'methods', if that makes sense. That is, I think constructing and updating models using Bayes' theorem can be (as people doing spam-filtering have shown) a good way of making predictions, but that it is the frequentist properties of the models that actually matter (cf. the ubiquity of cross-validation: 'the proof of the pudding is in the eating'), not the fact that they let you maintain a probability distribution over parameters.
