The result is my own work, but the reasoning is not particularly complex, and might well have been done before.
It’s kind of a poor man’s version of the central limit theorem, for differing distributions.
By this I mean that it’s known that if you take the mean of identical independent distributions, it will tend to a narrow spike as the number of distributions increase. This post shows that similar things happen with non-identical distributions, if we bound the variances.
And please do point out any errors that anyone finds!
The result is my own work, but the reasoning is not particularly complex, and might well have been done before.
It’s kind of a poor man’s version of the central limit theorem, for differing distributions.
By this I mean that it’s known that if you take the mean of identical independent distributions, it will tend to a narrow spike as the number of distributions increase. This post shows that similar things happen with non-identical distributions, if we bound the variances.
And please do point out any errors that anyone finds!