Finite size corrections for neural network Gaussian processes — mentioned by antognini

Thu, 28 Nov 2019 05:53:29 GMT

If you take the sum of a large, but finite number of random variates from a probability distribution you can show that the resulting distribution is approximately a Gaussian perturbed by Hermite polynomials. The size of these perturbations decays inversely with the order of the polynomial divided by two minus one. This is the so-called Edgeworth expansion. I found this chapter to have a good explanation of the Edgeworth expansion if you're interested in more detail: http://web.math.ku.dk/~erhansen/bootstrap_05/doku/noter/Edge... I had a little workshop paper earlier this year showing that you…

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Finite size corrections for neural network Gaussian processes · HackerPapers discussions

Finite size corrections for neural network Gaussian processes — mentioned by antognini