Course detail

LCE5700 - Geoestatistics

Credit hours

In-class work
per week
per week
5 weeks
120 hours

Paulo Justiniano Ribeiro Junior
Sonia Maria de Stefano Piedade

Definition and characterization of spatial statistics problems with emphasis on geostatistics.
To present models and spatial data analysis techniques for spatially continuous processes aiming for both, theoretical basics for the subject and usage of computational resources for planning the data aquisition and the practice of the statistical modelling, data analysis and criticism. The course shall include principles os statistical inference relevant to the spatial data analysis such that likelihood and Bayesian inference.

1. a) Motivation – examples of different areas of spatial statistics and charicterization of geostatistics, b) historical development of the field, applications areas and future directions. 2. Descriptive analysis: a)univariate, b) multivariate, c) spatial, d)computational aspects and data examples. 3. Elements of probability and statistics a) random variablas and uni and multivariate distributions, b) simulation, c) statistical inference – estimation and topics on likelihood analysis, d) linear models, e) computational aspects and illustrations. 4. Conventional geostatistics: a) variograms, b) spatial prediction, c) kriging I- basic methods, II- advanced methods, e) geostatistical simulation f) computational aspects and illustrations. 5. Univariate Gaussian geostatistical models: a) definition and properties, b) justifications c) inference, d) prediction, e) computational aspects and illustrations. 6. Bayesian geostatistics: a) elements of Bayesian inference, b) model specification, c) uncertainty levels, d) estimation, e) prediction, f) relations with conventional methods, g) computational aspects and illustrations.

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