Modern Spatiotemporal GeostatisticsThis introductory scholarly treatment explores the fundamentals of modern geostatistics, a group of spatiotemporal concepts and methods related to the advancement of the epistemic status of stochastic data analysis. Christakos considers the role of geostatistics in improved mathematical models of scientific mapping, and focuses on the Bayesian maximum entropy approach for studying spatiotemporal distributions of natural variables. 2000 edition. |
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applications associated BME analysis BME approach BME equations BME estimate BME mapping BMEmean BMEmode estimate Chapter Christakos concepts confidence intervals considered coordinate system covariance functions covariance model curvilinear coordinates Darcy's law data points defined denotes discussed distance domain epistemic estimation error estimation points Euclidean Euclidean geometry exposure Figure formulation fractal function Gaussian geostatisticians hard data hydraulic head integration involved knowledge bases kriging Lagrange multipliers mathematical means measurements meta-prior stage methods MMSE modern geostatistics modern spatiotemporal geostatistics multipoint natural variable obtained parameters physical knowledge physical laws Pmap point Pk posterior pdf Postulate prediction prior stage Prob probabilistic probability Proposition random field S/TRF scientific single-point situation soft data space space/time space/time points spatial spatiotemporal geometry spatiotemporal mapping specificatory knowledge statistics stochastic techniques temporal theoretical theory values variogram vector water-level elevation wavelet Xhard Xmap Xsoft