AI-GEOSTATSMean and variance estimation by kriginghttp://arxiv.org/abs/cs.NA/0409033Kriging Scenario For Capital Marketshttp://arxiv.org/abs/cs.CE/0509012Complex Mean and Variance of Linear Regression Model for High-NoisedSystems by Kriginghttp://arxiv.org/abs/cs.NA/0505015Best Regards,
Dear Jan W Merks"Surely, first generation geostatisticians can neither accept nor admit that each distance-weighted average-cum-kriged estimate has its own variance. Would future generations want to be wrong?"Even if the weighted average has its own variance it has no meaning.For two reasons:First.
Dear brothers in kriging They say that present efficiency of geostatistics in metal mines is (only) about 50%.I would say : Cause
we may neglect effect of dynamic ore formation. For the kriging equations these two correlograms + 1 if h = 0 a) r (h)
Dear Jan W Merks I have a simple question that should be a piece of cake for such great expert like You. For a set of N input samples I can do by kriging the only ONE estimate and compute kriging variance for this single estimate. So, why do You call the kriging variance the variance of so
Dear Jan W Merks KRIGING VARIANCEFor a set of N input samples I can do by kriging the only ONE estimate and compute kriging variance for this single estimate.So, kriging variance is a variance (derived from the model) of single unknown true value minus single weighted average.Krig
Dear Jan W Merks ABSTRACT (as You wish): Yes each distance-weighted average that is considered as the central value has its own variance but no each distance-weighted average has to have its own variance since no each distance-weighted average can be considered as the central value. BOD
merges from the mathematical model. However it implies that one can not just say: Yes, each kriging predictor has its own variance, but that variance is irrelevant for the description of the precession of the prediction. Best regards,Gerald v.d. BoogaartAm Donnerstag, 27. Juli 2006 10:05 schrieb tom andrews
Dear MarioI agree with You.Suppose that ordinary kriging predicts that I am a dwarf (or giant) with the height H. Since for e.g. gaussian distribution holds P(H)=0 we have to introduce P(H - s < h < H + s) = p where s is a square root of kriging variance and p is an area under t
Dear List For me kriging variance may convey distribution of random variable (the same for each random variable). Ok, I'm not going to argue what kriging variance really is. I can imagine mean (central value) of gaussian distribution +/- sigma. I can imagine that sigma can be multip
Dear List Let us consider theoretical model of stationary random function with some correlation function. Model and correlation function are out of any doubts. First generation of outcome values does not have to cover the second and go on. It means that "true" outcome value from first g
Dear Masters
I have rewritten a bit KV
http://arxiv.org/abs/cs.NA/0609079
Pax Romana
tom
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Dear Suzanne
File may call to generate color contour plot from array of input data.
Eye is eye.
Best regards
tom
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