Posterior mean

Mean of the posterior probability distibution in Bayesian inference.

It's formal definition uses an integral:

E [μ | y] = \int μ p (μ | y) d μ

But it can also be estimated using summation over a sample from the posterior probability distribution:

E [μ | y] \approx \frac{1}{n_{s a m p l e}} \sum_{k = 1}^{n_{s a m p l e}} μ_{s a m p l e, k}

(AKA the mean of all the $k$ samples of the posterior distribution)