I understand Bayesian look at the quantile. But is it useful to look at the posterior mean and posterior median?
How to explain it in the plain English?
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$\begingroup$ You have to consider Bayesian decision theory, ie the addition of a loss function to the Bayesian framework. $\endgroup$– Xi'anDec 13, 2020 at 14:47
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$\begingroup$ When talking about a distribution, we often use summary statistics, such as mean, median, sd, ... In Bayesian, after observing the data, each parameter has a posterior distribution. From this distribution, any summary has prefix posterior. Therefore, we have terms "posterior mean", ... $\endgroup$– TrungDungDec 13, 2020 at 20:33
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$\begingroup$ To know if something is meaningful, we must know what you want to do, your goal. You didn't tell us! $\endgroup$– kjetil b halvorsen ♦Dec 13, 2020 at 20:34
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