OK, here's my $0.02. I've used the convolution of an exponential tail function
exp(-(r-r0)/w) (r-r0)*w >= 0
0 (r-r0)*r < 0
with a Gaussian. This avoids having to have parameters go to infinity to approach a gaussian. This function
is a little unwieldy in real space but is simple in k-space.
mam
----- Original Message -----
From: "grant bunker"
re the cumulant <--> rdf connection This isn't a general solution, but it may work well for your system. A poisson distribution (x-x)^s Exp[-a (x-x0)] is a pretty flexible for moderately skewed distributions, permitting variable C2 and C3 by adjusting parameters s and a. For large s and small a the distribution approaches a gaussian. Expressions for cumulants etc are in Yang et al, JNCS Volume 210, Number 2, March 1997
have fun - gb
matt,
I have a problem with an exafs analysis: from the exafs analysis (with feffit2.98) of the first shell of a disordered system I get the first three cumulants. now, I wander how can reconstruct from them the radial distribution function which is the best solution? I have tried with the skew-normal distribution, but for the high skewness (ratio of third cumulant to sigma3) values I get - that is around 2.5 - it is not well defined. Do you have a suggestion? Thanks,
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