Thanks for your interest. An issue that recently came up in the Probability module is sampling from a Poisson distribution <https://en.wikipedia.org/wiki/Poisson_distribution>. It used to not work at all, and now it does but the algorithm is not efficient when the parameter lamda is large. For example:
from sympy.stats import * sample(Poisson('x', 1000)) can take a while to return. Usually one can sample by generating a Uniform(0, 1) random number u, and then apply the inverse of the cumulative distribution function (CDF) to u. But there isn't a formula for the inverse of the of a Poisson random variable. The current algorithm <https://github.com/sympy/sympy/blob/master/sympy/stats/drv_types.py#L31> simply goes over all integers looking for the first one where CDF(n) >= u. There ought to be a better way of doing this. The first idea that comes to mind is to make giant steps (in power of 2) until CDF(n) >= u is reached, and then refine by bisection. But perhaps it's better to do research first, there is probably an algorithm out there that we can use. Maybe R has it? https://www.r-project.org/ -- You received this message because you are subscribed to the Google Groups "sympy" group. To unsubscribe from this group and stop receiving emails from it, send an email to sympy+unsubscr...@googlegroups.com. To post to this group, send email to sympy@googlegroups.com. Visit this group at https://groups.google.com/group/sympy. To view this discussion on the web visit https://groups.google.com/d/msgid/sympy/8f337698-5ba7-40dc-9273-3dc47207acd3%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.