Olumide I would think what they mean is that each order of polynomial has to be balanced between the 'drift' at the actual estimated point and the weighted average of the samples which proovides the estimator. For this you have to introduce an extra lamda and an extra equation on the kriging system which guarantees the unbiassedness of the estimate. At least, that is what happens in Universal Kriging. What is "annihilated" is any possible bias due to the order k. I do not know why lamda is referred to as a "discrete measure". Isobel http://www.kriging.com/courses
Olumide <[EMAIL PROTECTED]> wrote: Hello - I've made some progress understanding what intrinsic random functions are, and what increments are in that regard. The next question that's still puzzling me is the question of what the discrete measure lambda and the annihilation of polynomials. Quote from "Geostatistics Modeling Uncertainty" by Chiles and Delfiner page 238: "Definition: a discrete measure lambda is allowable at the order k if it annihilates polynomials of degree less than or equal to k" Questions: 1. what does it mean for lambda to annihilate a polynomial 2. why the need to annihilate those "poor" polynomials (what have they done wrong? ;-) ) Thanks, - Olumide + + To post a message to the list, send it to [email protected] + To unsubscribe, send email to majordomo@ jrc.it with no subject and "unsubscribe ai-geostats" in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions. + Support to the forum can be found at http://www.ai-geostats.org/
