The central limit theorem states that, under certain circumstances, the probability distribution of the scaled mean of a random sample converges to a normal distribution as the sample size increases to infinity. Under stronger assumptions, the Berry–Esseen theorem, or Berry–Esseen inequality, gives a more quantitative result, because it also specifies the rate at which this convergence takes place by giving a bound on the maximal error of approximation between the normal distribution and the true distribution of the scaled sample mean.

In free probability, analogue of Central Limit Theorem is known where Wigner's semi-circle law plays the role of Normal distribution. Berry–Esseen type theorem for free random variables is due to Vladislav Kargin 2007.

In monotone probability, analogue of Central Limit theorem is also known where arc-sine law plays the role of normal distribution.

*But the Berry-Esseen type theorem seems to be missing.. Is it just an open problem or are there known reasons for which such a result won't be possible*....thanks....