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Aaron Meurer


On Fri, Apr 4, 2014 at 4:17 PM, Matthew Rocklin <mrock...@gmail.com> wrote:

> Done.  You should announce that tip to the list.  There were indeed some
> good questions on there.  SO is a good way to keep our collective ear to
> the ground.
>
>
> On Fri, Apr 4, 2014 at 1:48 PM, Aaron Meurer <asmeu...@gmail.com> wrote:
>
>>  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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>>>
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>>
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>
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