Given that bayesian probabilites are updated over time, how is this represented in a probability space? More specifically, how do we interpret the probability measure of such a space? That is, presumably such a probability measure is not preserved over time, whereas in frequentist statistics this is assumed to be invariant, so is a bayesian probability measure 'time-varying'?
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In a frequentist world, if you take another sample, then you get other estimates, isn't that the same ? – Oct 09 '16 at 15:09
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There really are no "Bayesian" probabilities, so I'm not sure what you mean. The term is just used to refer to a certain interpretation of probability, but the mathematics does not change in any way. – dsaxton Oct 09 '16 at 15:35
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You may be interested in checking more general thread: http://stats.stackexchange.com/questions/173056/how-exactly-do-bayesians-define-or-interpret-probability/237772#237772 – Tim Oct 09 '16 at 16:03