On Sunday, 8 October 2017 01:10:14 UTC-4, Francesco Bonazzi wrote:
>
> This result is definitely wrong.
>
Sorry, ignore my previous answer, that result is correct. If you sum *n + n*,
you are summing the same random value. To get the effect of summing two
uniform distributions, just define a n
This result is definitely wrong.
I have opened an issue:
https://github.com/sympy/sympy/issues/13417
On Saturday, 7 October 2017 20:46:34 UTC-4, EKW wrote:
>
> Consider:
>
>
> ```
>
> >>> from sympy import *
> >>> from sympy.stats import *
> >>> t = Symbol('t')
> >>> n = Uniform('n', 0, 1)
> >>>
I am computer science undergraduate, I am intermediate in c++,python. I would
like to contribute something to this. This would be my first open source
project. Can someone please guide me, which part I can work with and how to
start.
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On Saturday, 7 October 2017 12:30:16 UTC-4, Robert Dodier wrote:
>
> Hi Francesco, thanks for your response. The Rubi implementation looks very
> interesting, also the paper about matching algorithms. I will definitely
> take a close look at that.
>
Yes, the MatchPy authors have published a p
Consider:
```
>>> from sympy import *
>>> from sympy.stats import *
>>> t = Symbol('t')
>>> n = Uniform('n', 0, 1)
>>> print(density(n + n)(t))
Piecewise((1, (0 <= t/2) & (t/2 <= 1)), (0, True))/2
```
This is a uniform distribution from 0 to 2.
Am I misunderstanding what the sum of two r
Hi Francesco, thanks for your response. The Rubi implementation looks very
interesting, also the paper about matching algorithms. I will definitely
take a close look at that.
What is your feeling about how the Rubi implementation turned out overall?
Did you run into any roadblocks on the way? D
I want to differentiate an expression as follows in `sympy`. I am using
`jupyter qtconsole` with enabled latex:
from sympy import *
init_printing()
p0,nu,lamb,k,epsp, dp0 = symbols(r'p_0, nu, lambda, kappa,
\delta\epsilon_p^p, \deltap_0')
test = Eq(epsp,((lamb-k)/nu)*dp0/p0)
