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AI-GEOSTATS: generating 2-dim point pattern

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  • p.bossew@magnet.at
    dear list, The following may be slightly off topic (in which case I apologize), but maybe somebody can help me: I want to generate a set of points in a plane,
    Message 1 of 2 , Mar 9, 2003
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      dear list,

      The following may be slightly off topic (in which case I apologize), but
      maybe somebody can help me:

      I want to generate a set of points in a plane, {(xi,yi)}, such that the
      points are distributed according to a chosen probability distribution
      density f, i.e., the infinitesimal rectangle [x,x+dx] X [y,y+dy] shall
      contain f(x,y)*dx*dy points. How do I do this ??

      In other words, I am looking for a generalization of the 1-dim method,
      where this is done with F*(z), where F=cumul(f) and F* = inverse of F, and
      z = random out of [0,1] (or any appropriate interval if f is not
      normalized).

      I tried to do it the same way, just using complex numbers, but this does
      not seem to work, and could also not be generalized for n>2 dimensions.

      thanx for any hint,
      p

      (PS. sorry if it is trivial.)
      --------------------------------------------------------------------------------------
      Peter Bossew
      Institute of Physics and Biophysics, University of Salzburg, Austria

      home: Georg Sigl-Gasse 13/11, A-1090 Vienna, Austria
      ph. +43-1-3177627, e-mail: p.bossew@... , peter.bossew@...

      =========================================================


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    • Ted Harding
      ... Hi Peter, The problem with doing it the same way is that the same way only applies in 1 dimension (as a consequence of the result that F(X) has a
      Message 2 of 2 , Mar 10, 2003
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        On 10-Mar-03 Peter Bossew wrote:
        > I want to generate a set of points in a plane, {(xi,yi)}, such
        > that the points are distributed according to a chosen probability
        > distribution density f, i.e., the infinitesimal rectangle
        > [x,x+dx] X [y,y+dy] shall contain f(x,y)*dx*dy points.
        > How do I do this ??
        >
        > In other words, I am looking for a generalization of the 1-dim method,
        > where this is done with F*(z), where F=cumul(f) and F* = inverse of F,
        > and z = random out of [0,1] (or any appropriate interval if f is not
        > normalized).
        >
        > I tried to do it the same way, just using complex numbers, but this
        > does not seem to work, and could also not be generalized for n>2
        > dimensions.

        Hi Peter,
        The problem with "doing it the same way" is that "the same way" only
        applies in 1 dimension (as a consequence of the result that F(X) has
        a uniform distribution, so that F*(X) has the distribution of X).
        In more than 1 dimension, you still only have the result that F(X,Y)
        has a uniform distribution, and this is only 1 equation. There is
        no _straightforward_ way of basing it on two equations

        (F1(X,Y), F2(X,Y)) = (Z1,Z1) where (Z1,Z2) is uniform on [0,1]x[0,1]

        However, there are possible approaches. One is to use conditional
        distributions: let F(X) be the marginal distribution for X, and let
        G(Y;x) be the conditional distribution of Y given that X=x. Then
        both U=F(X) and, for each x, V=G(Y;x) have uniform distributions,
        so provided you can invert these as F*(U) and G*(V;x) you can then
        sample U=u,V=v and obtain x=F*(u) and then y=G*(v;x).

        The practical issues here depend a lot on the form of F(X,Y),
        since both obtaining the conditional distribution and inverting
        the functions F(X), G(Y;x) may be difficult in practice.

        If it is straightforward to obtain the conditional distributions
        for Y given X and X given Y, then Gibbs Sampling can enable you to
        sample from (X,Y) without inverting the functions.

        If you can't obtain the conditional distributions then it may be
        impossible to follow such approaches, and then you may need to fall
        back on simulating a random process with a known mechanism which has
        the property of yielding (X,Y) distributed as f(x,y) -- if you know
        of such a process!

        If you could describe the density f(x,y) you are trying to sample
        from to the list, then perhaps someone can suggest an approach.

        > (PS. sorry if it is trivial.)

        No, it isn't trivial!

        Best wishes,
        Ted.


        --------------------------------------------------------------------
        E-Mail: (Ted Harding) <Ted.Harding@...>
        Fax-to-email: +44 (0)870 167 1972
        Date: 10-Mar-03 Time: 08:29:19
        ------------------------------ XFMail ------------------------------

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