Bayesian technique says that "probability" is a way of representing our collective knowledge and opinions about some matter. To say that there's a 1/2 chance of

Bayesian technique says that "probability" is a way of representing our collective knowledge and opinions about some matter. To say that there's a 1/2 chance of heads is to say that to the best of our knowledge and understanding we have no way to expect one outcome over the other. It lives in the present. Frequentist technique says that "probability" is a way of describing repetition in experimentation. To say there's a 1/2 chance of heads is to say that should we flip it 100 times the best guess as to the number of heads we might obtain is (1/2)(100). It lives in a counterfactual future. This is a foundation of the debate. The two schools are actually trying to accomplish different things. It turns out that much of the time these two endeavors land on identical solutions. It also turns out that in human experience we often flip between the two intentions without notice. It's not possible to say one is better than the other unless you pick one semantics or the other to argue from (after which, obviously the other is poor). Instead, it's viable to look at what you're really trying to accomplish—often a task where errors and computation have expense and a tradeoff exists between accuracy, speed, information use, communicability, etc etc—and understand how each approach helps or harms your end. Good statisticians, usually no matter how much they punch for one side or the other, will make these tradeoffs and use the technology which achieves the best end. But we're all also at least a little bit philosophers and epistemologists so it's impossible to not want to take a few swings for one side or the other. Especially with historical figures like Jaynes leading the way.