One useful definition of Bayesian vs Frequentist that I’ve found is the following. Suppose you run an experiment; you have a hypothesis and you gather some data.
if you try to obtain the probability of the data, given your hypothesis (treating the hypothesis as fixed), then you’re doing it the frequentist way
if you try to obtain the probability of the hypothesis, given the data you have, then you’re doing it the Bayesian way.
I’m not sure whether this view holds up to criticism, but if so, I sure find the latter much more interesting than the former.
Interesting that, very often, people interpret a frequentist result as though it were Bayesian. E.g. that there is a 90% chance the true value is within the confidence interval. This is so common in medical research that it may possibly be the majority interpretation.
One useful definition of Bayesian vs Frequentist that I’ve found is the following. Suppose you run an experiment; you have a hypothesis and you gather some data.
if you try to obtain the probability of the data, given your hypothesis (treating the hypothesis as fixed), then you’re doing it the frequentist way
if you try to obtain the probability of the hypothesis, given the data you have, then you’re doing it the Bayesian way.
I’m not sure whether this view holds up to criticism, but if so, I sure find the latter much more interesting than the former.
Interesting that, very often, people interpret a frequentist result as though it were Bayesian. E.g. that there is a 90% chance the true value is within the confidence interval. This is so common in medical research that it may possibly be the majority interpretation.