Thanks Leonid, for pointing me to this issue. I will start looking into it. What is the issue id?
With Regards, Shilpa. P.S.: I didn't get a notification of your reply to my e-mail. Is there any settings that I need to do? On Tuesday, January 23, 2018 at 12:04:50 AM UTC+5:30, Leonid Kovalev wrote: > > 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/0cb65d20-5126-4387-8aa4-f2e5ec1b82cc%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
