I was planning to be a part of GSOC this summer with SymPy to learn more and implement something big.
*For this,I was planning to work on improving SymPy's stats module;* I checked there are many good things already implemented in the stats module. And, I found issue(https://github.com/sympy/sympy/issues/17197) about the Random Walk implementation which is in progress, but it seems the issue created is closed for now and I am looking forward to complete it. On the ideas page of SymPy for GSOC in the Probability section I found things which can be implemented as a part of GSOC project. I saw things there which interests me to work on: --> Reproducibility of Sampling Outputs of Stochastic Processes, WienerProcess(I think the idea of ito calculus is covered here), Completing Random Walk(I think it was a typo there it should be Prototype not Protorype), etc. *But my query is:I was planning to implement something different;A better way to use probability of events like one mentioned here(https://github.com/sympy/sympy/issues/20111):* --> *Working with events rather than random variables.--> Currently I don't think there's is a way to define just an event andnot specifically a random variable like* A, B = event('A, B') P(A) = x P(B & A) = y P(B & !A)? *Also, currently there is not a way to assign probability like a certain event A* *has probability P(A) = x(Please enlighten me if I am making a mistake somewhere ; ) )* and noise processes too, like white noise (White noise refers to a statistical model for signals and signal sources, rather than to any specific signal), *is it possible to work on some of the things from ideas page and some from our own?* *[I am trying to understand the codebase now(Are there any tips which could help me to understand things more efficiently? ) *: ( *It may not be a valid question though, but thought I should try to ask first.]* -- 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 [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/sympy/65fb05d7-403f-4ad4-ba2c-4b270e3e953an%40googlegroups.com.
