[SciPy-User] characteristic functions of probability distributions
josef.pktd@gmai...
josef.pktd@gmai...
Thu Nov 5 23:02:47 CST 2009
On Thu, Nov 5, 2009 at 11:19 PM, Anne Archibald
<peridot.faceted@gmail.com> wrote:
> 2009/11/5 David Goldsmith <d.l.goldsmith@gmail.com>:
>> On Mon, Nov 2, 2009 at 12:51 PM, nicky van foreest <vanforeest@gmail.com>
>> wrote:
>>>
>>> Hi Josef,
>>> > Second related question, since I'm not good with complex numbers.
>>> >
>>> > scipy.integrate.quad of a complex function returns the absolute value.
>>> > Is there a numerical integration function in scipy that returns the
>>> > complex integral or do I have to integrate the real and imaginary
>>> > parts separately?
>>>
>>> You want to compute \int_w^z f(t) dt? When f is analytic (i.e.,
>>> satisfies the Cauchy Riemann equations) this integral is path
>>> independent. Otherwise the path from w to z is of importance. You
>>> might like the book Visual Complex Analysis by Needham for intuition.
>>
>> Furthermore, if f is analytic in an (open) region R homotopic to an (open)
>> disc, then the integral (an integer number of times) around *any* _closed_
>> path wholly in R is identically equal to zero; there's a similar statement
>> (though the end value is a multiple of 2ipi) if f has only poles of finite
>> order in R. (Indeed, these properties should be used to unit test any
>> numerical complex path integration routine.) Are any of your paths closed?
>
> This may well be a red herring. It happens fairly often (to me at
> least) that I want to integrate or otherwise manipulate a function
> whose values are complex but whose independent variable is real.
>
> Such a function can arise by substituting a path into an analytic
> function, but there are potentially many other ways to get such a
> thing - for example you might choose to represent some random function
> R -> R2 as R -> C instead. Even if it's obtained by feeding a path
> into some function from C -> C, it happens very often that that
> function isn't analytic - say it involves an absolute value, or
> involves the complex conjugate.
>
> There are definitely situations in which all the clever machinery of
> analytic functions can be applied to integration problems (or for that
> matter, contour integration may be the best way available to evaluate
> some complex function), but there are also plenty of situations where
> what you want is just a real function whose values happen to be
> complex numbers. (Or vectors of length n for that matter.) But I don't
> think that any of the adaptive quadrature gizmos can handle such a
> case, so you might be stuck integrating the real and imaginary parts
> separately.
>
> If you *are* in a situation where you're dealing with an analytic
> function, then as long as you're well away from its poles and your
> path is nice enough, you may find that it's very well approximated by
> a polynomial of high degree, which will let you use Gaussian
> quadrature, which can very easily work with complex-valued functions.
> The Romberg integration might even work unmodified.
Sorry for not coming back to this earlier,
Thanks Nicky, I looked at some papers by Ward Whitt and they look
interesting but much more than what I want to chew on right now. There
is more background, that I would have to read, than I have time right
now for this. I finally added "matlab" to my google searches, and I
think I found some references that use discretization and fft more
directly.
The integration problem should be pretty "nice", just a continuous
fourier transform and the inverse
http://en.wikipedia.org/wiki/Characteristic_function_%28probability_theory%29#Definition
http://en.wikipedia.org/wiki/Characteristic_function_%28probability_theory%29#Inversion_formulas
For many distributions there is an explicit formula for both the
density and the characteristic function, e.g. normal
http://en.wikipedia.org/wiki/Normal_distribution#Characteristic_function
For some distributions only the characteristic functions has a closed
form expression, and the pdf or cdf has to be recovered numerically,
and I would have liked to have a generic method to go between the two.
I don't think I ever needed a path integral in my life, and I'm pretty
much a newbie to complex numbers, so parts of your explanations are
still quite a bit over my head. I think, I will come back to this
after I looked more at the examples where the estimation of a
statistical model or of a distribution is done in terms of the
characteristic function instead of the density.
The immediate example that I had tried, was (integration from -large
number to +large number)
integral exp(i t x)dF(x) = integrate.quad(exp(itx)*f(x))
or do I have to do
integral exp(i t x)dF(x) = integrate.quad(real(exp(itx)*f(x))) + j *
integrate.quad(imag(exp(itx)*f(x)))
or is there another way?
The solution/integral might be either real or complex.
Thanks,
Josef
>
> Anne
>
>> DG
>>>
>>> bye
>>>
>>> Nicky
>>>
>>> >
>>> > Thanks,
>>> >
>>> > Josef
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