> By the way, I have been using the sympy.stats module, and I get a lot of
*NotImplementedError*, and other kinds of errors. Finishing that parts
would be great, too.

I'd be curious to know how you're using it and what unimplemented features
are important to you.  This discussion should maybe happen off of this
thread though.


On Tue, Mar 4, 2014 at 9:02 AM, F. B. <[email protected]> wrote:

>
>
> On Tuesday, March 4, 2014 6:18:18 AM UTC+1, Chase Relock wrote:
>>
>> I was looking at SymPy's matrix code and was curious if that supports
>> symbolic computation right now or is it only numeric?
>>
>
> Of course you can put symbols inside sympy matrices (if that is what you
> mean), for numeric-only ones, have a look at numpy (technically numpy could
> be forced to use symbols, but it's not straightforward).
>
>
>> As for some of the individual pieces, I think the following are important
>> concepts needed in financial statistics that I am unsure SymPy currently
>> has functionality for:
>> kernel density estimators for PDFs
>> Empirical distribution functions
>> Statistical moments
>> Stochastic Processes
>> Stochastic/Ito Calculus
>> Time series
>> Matrix decomposition (spectral, singular value)
>>
>>
> What about introducing stochastic processes, Martingales and Ito
> integration (all in symbolic representation, of course)? I think that could
> already be a good project.
>
> By the way, I have been using the sympy.stats module, and I get a lot of
> *NotImplementedError*, and other kinds of errors. Finishing that parts
> would be great, too.
>
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