Given the potentially large differences in estimated probabilities based on frequentist versus
Bayesian interpretations, witnesses should be explicit not only about the set of results from which the estimate is derived, but also about which interpretation is being used to generate the probability estimate.
Fred, I was referring to
the Bayesian interpretation in an earlier post.
It is possible to recast an OLS - regression, normal - error - distribution based study in Bayesian terms, but there is generally little point in doing so since the regression model and error distributions uniquely define the form of the prior distribution appropriate for
a Bayesian interpretation.
(although I may not have
my Bayesian interpretation straight here...)
Under
the Bayesian interpretation, the probability of an outcome is a measure of our belief that, in the experiment in question, a particular outcome will result.
Not exact matches
Fred wrote:
Bayesian statistics are extremely useful in medical diagnosis, among other areas, including the
interpretation of test results.
The estimated probability of an event or hypothesis under a frequentist
interpretation of probability may differ dramatically from that estimated under a
Bayesian approach.
The issue of which
interpretation (
Bayesian or frequentist) is more appropriate in a given situation is not primarily a scientific question.
Probability is a superficially simple concept for which two principal
interpretations are employed in science: frequentist and
Bayesian.
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Bayesian credible intervals have a different
interpretation than classical confidence intervals because they are not defined in terms of frequentist coverage, but in terms of the researcher's (possibly subjective) posterior certainty about the parameter.