Hi Joe,

After the backtransform, the distribution of simulated values
at each node is not Gaussian anymore and its variance can be
used as a local index of uncertainty, which accounts for both
the range of surrounding values and their closeness in
terms of data configuration.

Regards,

Pierre Goovaerts

<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

Dr. Pierre Goovaerts
President of PGeostat, LLC
Chief Scientist with Biomedware Inc.
710 Ridgemont Lane
Ann Arbor, Michigan, 48103-1535, U.S.A.

E-mail:  [EMAIL PROTECTED]
Phone:   (734) 668-9900
Fax:     (734) 668-7788
http://alumni.engin.umich.edu/~goovaert/

<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

On Mon, 9 Jun 2003, Joe Geo wrote:

> Dear ai-geostats
>
> A question about simulation
>
> I understand in the simulation methods SGS that each node the data a kriging
> is performed than an value is drawn from a normal distribution with the
> local mean (from data and previously simulated nodes captured in a search
> neigbourhood) and variance defined by the kriging variance.
>
> My understanding of kriging variance is that this variance  is really an
> index of data configuration independent of the data values.  This leads to
> my question.
>
> How a set of multiple simulations capture information about the variability
> of conditioning data values when the kriging variance is only a data
> location index.  Specifically, it is possible to have the same conditioning
> data configuration but the variability of the values attached to the data
> can be quite different.  How is this recognised in the simulation process.
>
> Thanks
>
> Joe
>
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