Thanks for the pointer, Francesco. I am in the process of setting up my environment. Hope to start soon.
With Regards, Shilpa. On Wednesday, January 24, 2018 at 2:24:48 PM UTC+5:30, Francesco Bonazzi wrote: > > Some ideas for the probability module: > > > - hyperparameters > - random matrices > - stochastic processes (through indexed random variables) > > > On Tuesday, 23 January 2018 12:03:33 UTC+1, Leonid Kovalev wrote: >> >> There is a discussion at https://github.com/sympy/sympy/pull/13943 but >> no separate issue (yet). >> >> On Jan 23, 2018 5:27 AM, "Shilpa Sangappa" <shilpa....@gmail.com> wrote: >> >>> 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+un...@googlegroups.com. >>> To post to this group, send email to sy...@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 >>> >>> <https://groups.google.com/d/msgid/sympy/0cb65d20-5126-4387-8aa4-f2e5ec1b82cc%40googlegroups.com?utm_medium=email&utm_source=footer> >>> . >>> For more options, visit https://groups.google.com/d/optout. >>> >> -- 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/5fbcfff7-01b9-47a4-a090-8081c1d09460%40googlegroups.com. For more options, visit https://groups.google.com/d/optout.
