Yes, you can go to http://stackexchange.com/filters/ and set up a filter
for the SymPy tag, and it will email you new questions. Lately there are
new questions almost every single day, so it would definitely help if
others were answering there as well.
The questions on stackoverflow tend to be high quality and often have
rather interesting use-cases (there are doozies, of course, but the worst
questions are closed automatically).
Aaron Meurer
On Tue, Apr 1, 2014 at 9:48 AM, Matthew Rocklin <mrock...@gmail.com> wrote:
> First, thanks for posting the question on stack overflow, I think that we
> should use that more. Unfortunately, few of us (maybe only Aaron?)
> actually checks SO, again, I think that we should use it more.
>
> Sympy.stats is producing an integral that looks like the following:
> In [1]: from sympy.stats import *
> In [2]: a, b, c, d, = symbols('a b c d', real=True, positive=True)
> In [3]: G1 = GammaInverse("G1", a, b)
> In [4]: G2 = GammaInverse("G2", c, d)
> In [5]: G3 = S(7)/10*G1 + S(3)/10*G2
> In [7]: density(G3, evaluate=False)(x)
> Out[7]:
> ∞
>
> ⌠
>
> ⎮ ∞
>
> ⎮ ⌠
>
> ⎮ ⎮ -b
>
> ⎮ ⎮ ───
>
> ⎮ -d ⎮ -a - 1 a G₁ ⎛7⋅G₁ 3⋅G₂ ⎞
>
> ⎮ ─── ⎮ G₁ ⋅b ⋅ℯ ⋅DiracDelta⎜──── + ──── - x⎟
>
> ⎮ -c - 1 c G₂ ⎮ ⎝ 10 10 ⎠
>
> ⎮ G₂ ⋅d ⋅ℯ ⋅⎮ ──────────────────────────────────────────── d(G₁)
>
> ⎮ ⎮ Γ(a)
>
> ⎮ ⌡
>
> ⎮ 0
>
> ⎮ ─────────────────────────────────────────────────────────────────────
> d(G₂)
> ⎮ Γ(c)
>
> ⌡
>
> 0
>
>
> So one could reduce your question into a question like, "does anyone have
> any thoughts on how SymPy could solve this integral?"
>
>
> On Tue, Apr 1, 2014 at 6:26 AM, John Griffiths <j.davidgriffi...@gmail.com
> > wrote:
>
>>
>>
>> Does anyone have any thoughts on how to solve this problem:
>>
>> When I try to take a weighted mixture of inverse gamma distributions
>> using sympy.stats I get the following error
>>
>> %matplotlib inlinefrom matplotlib import pyplot as pltfrom sympy.stats
>> import GammaInverse, densityimport numpy as np
>>
>> f1 = 0.7; f2 = 1-f1
>> G1 = GammaInverse("G1", 5, 120/(5.5*2.5E-7))
>> G2 = GammaInverse("G2", 4, 120/(5.5*1.5E-7))
>> G3 = f1*G1 + f2*G2
>> D1 = density(G1);
>> D2 = density(G2);
>> D3 = density(G3);
>> v1 = [D1(i).evalf() for i in u]
>> v2 = [D2(i).evalf() for i in u]
>> v3 = [D3(i).evalf() for i in u]
>>
>> ...which errors at D3 = density(G3). The error includes
>>
>> PolynomialDivisionFailed: couldn't reduce degree in a polynomial
>> division algorithm when dividing [231761.370742578/(0.0011381138741823*G2**2
>> -
>> 0.007587425827882*G2*_z + 0.0126457097131367*_z**2), 0.0]
>> by [263.770831541635/263.770831541635, 0.0].
>> This can happen when it's not possible to detect zero in the coefficient
>> domain. The domain of computation is RR(G2,_t0,_z). Zero detection is
>> guaranteed in this
>> coefficient domain. This may indicate a bug in SymPy or the domain is user
>> definedand doesn't implement zero detection properly.
>>
>> (also get this when I take mixture of inverse Gamma with Normal and
>> Uniform distributions)
>>
>> Should this be possible?
>>
>>
>> Cheers.
>>
>>
>>
>> (p.s. apologies for redundancy with recent SO post)
>>
>>
>>
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