The First Coming of Dr Roussos Dimitrakopoulos from Down Under all the way to McGill University came to pass in 1993. He had come to chair an International Forum on Geostatistics for the Next Century. The stage for this early forum was set at McGill’s Conference Office on June 3-5, 1993. Geostatistical scholars from far and wide had come to praise Professor Dr Michel David. For it was he who had crafted the very first textbook. His 1977 Geostatistical Ore Reserve Estimation does refer to “the famous central limit theorem”. Even degrees of freedom make a token appearance in Table 1.IV. His work did qualify for support from the National Research Council of Canada (Grant NRC7035). I do have my own copy of his book and have worried a lot about it. That’s why I mailed an abstract on “The Properties of Variances”. In fact, I did send it twice by registered mail. The first was lost and the second rejected. All I wanted to ask was why each and every distance-weighted average AKA kriged estimate did not have its own variance in his 1977 textbook. The more so since one-to-one correspondence between functions and variances is so sine qua non in my work!

David in November 1989 had rejected Merks and Merks Precision Estimates for Ore Reserves. He had done so because our paper was short on references to 20 years of geostatistical literature. In contrast, Erzmetall praised its *“splendid preparation”* and published it in October 1991. David didn’t know how to test for spatial dependence, how to count degrees of freedom, or how to derive unbiased confidence limits for gold grades and contents of in-situ ore. We did what David never got around to doing. We derived precision estimates for the mass of contained gold based on assays determined in a set of ordered rounds in a drift. The set of primary increments from each mined round had been put in the same basket. That’s why we couldn’t estimate the intrinsic variance of gold.

My son and I had taken at different times the same stats courses at Simon Fraser University. I had done so shortly after we came to Canada in October 1969. My problem in those days was that I spoke German and French better than English. I left SGS to work with Cominco. I wrote a lot on sampling and statistics and lectured all over the world. These days I still write about sampling and statistics but travel little. My son Ed has a PhD in Computing Science and was twice awarded the Dean’s Silver Medal. He worked with IBM in Toronto. Nowadays he leads the top-level Eclipse Modeling Project and the Eclipse Modeling Framework subproject. Ed set up Macro Modeling, a small but independent company. My wife and I are pleased that he and his partner have settled in Vancouver, BC. Applied statistics is still fun and games for both us. Here’s what keeps us thinking about McGill University.

**Variance of a general function**This formula finds its origin in calculus and probability theory. It shows that the population variance of a general function is the sum of n variance terms, each of which is the squared partial derivative toward an independent variable multiplied by its variance. It bridges the gap between probability theory with its infinite set of possible outcomes and sampling practice with its finite sets of measured values. The formula underpins the variance of any central value. The arithmetic mean is the central value of a set of measured values with constant weights. Area-, count-, density-, distance-, length-, mass-, and volume-weighted averages are central values of sets of measured values with variable weights. The transition from sampling theory to sampling practice with finite sets of measured values demands that degrees of freedom be counted. No ifs or buts! Functions without variances have gone where dodos fly!

In 1970 Professor Dr Georges Matheron brought his new science of geostatistics to North America. In his Random Functions and their Application in Geology Matheron invoked Brownian motion along a straight line. It was just as richly embellished with symbols and as short on primary data as is his magnum opus. Maréchal and Serra in Random Kriging applied the same symbols that Matheron had taught to all of his disciples. Figure 10 puts in plain view how to do more with less.

**Figure 10 – Grades of n samples belonging to
nine rectangles P of pattern surrounding x**

David may have thought that what Maréchal and Serra were doing was kind of cool. So, he explains it on page 286 of his 1977 textbook in Chapter 10 *The Practice of Kriging*. He dressed up M&S‘s Figure 10 with a slightly different caption.

**Fig. 203. Pattern showing all the points within B,**

which are estimated from the same nine holeswhich are estimated from the same nine holes

David added a dash of subterfuge when he called his points within B **“estimated”**. Each point within B derives from the same set of measured values for nine (9) holes. As such, each and every one of them is a function of the same set of nine (9) holes. Of course, each distance-weighted average does have its own variance in applied statistics. Thus it came about that variance-deprived distance-weighted average point grades morphed into kriged estimates.

**Matheronian madness made a mess in B**

In Section 12.2.1 Using a simulated model of Chapter 12 Orebody Modelling (see page 324) David prevaricates, *“The criticism to this model is obvious. The simulation is not reality. There is only one answer: The proof of the pudding is…!* *So far the few simulations made which it has been possible to check have a posteriori proved to be adequate”*. Good grief! Why didn’t they ask Merks and Merks?

McGill University had set the stage in 1993 to praise Professor Dr Michel David. Those who had come to its Conference Center to praise him did not have the faintest clue what was wrong with geostatistics. But rigs were drilling at Bre-X’s Busang property! The Bre-X fraud came about because the geostatocracy had failed to grasp the properties of variances.

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