Hi Pete,
This is a classical example where stochastic simulation would allow an easy
quantification
of the uncertainty attached to the aggregated value. Just generate a series of
realizations
of your process over these 1700 points, sum each set of simulated values, and
use the empirical distribution of simulated block values as a model of
uncertainty.
You can find an example in Goovaerts, P. 2001. Geostatistical modelling of
uncertainty in soil science. Geoderma, 103: 3-26.
<http://www.terraseer.com/training/geostats/geoder01.pdf> that you can
download from my webpage.
Cheers,
Pierre
-----Original Message-----
From: Pete Gething [mailto:[EMAIL PROTECTED]
Sent: Mon 8/1/2005 9:30 AM
To: [email protected]
Cc:
Subject: [ai-geostats] Sum of predicted values
Dear list,
I have Kriged predictions of a continuous variable at a set of 1700
points. I want to sum these values and obtain an estimate of the overall
prediction variance based on the kriging variances of the individual points
(i.e., taking into account the spatial correlation between points). The data
are approximately Gaussian.
I would expect there to be a standard solution to this problem, but I'm
having difficulty finding examples - can anyone help me out, or point me to a
reference?
Thanks in advance,
Pete
____________________________________________________________________
Peter Gething
School of Electronics and Computer Science
School of Geography
University of Southampton
Highfield
Southampton SO17 1BJ
UK
Tel: +44 (0) 23 8059 2013
Email: [EMAIL PROTECTED]
____________________________________________________________________
* By using the ai-geostats mailing list you agree to follow its rules
( see http://www.ai-geostats.org/help_ai-geostats.htm )
* To unsubscribe to ai-geostats, send the following in the subject or in the
body (plain text format) of an email message to [EMAIL PROTECTED]
Signoff ai-geostats
