I am aggregating arrays of Monte Carlo samples which have N samples each. There is a sense in which each sample is one point estimate, but for large N (I am using 10^7) I guess I can fit a distribution to each of the arrays.
Without more context, I’d say that fit a distribution to each array and then aggregate them using a weighted linear aggregate of the resulting CDFs, assigning a weight proportional to your confidence on the assumptions that produced the array.
Thanks!
I am aggregating arrays of Monte Carlo samples which have N samples each. There is a sense in which each sample is one point estimate, but for large N (I am using 10^7) I guess I can fit a distribution to each of the arrays.
Without more context, I’d say that fit a distribution to each array and then aggregate them using a weighted linear aggregate of the resulting CDFs, assigning a weight proportional to your confidence on the assumptions that produced the array.
Thank you. Feel free to check this for more context.