Statistics for Spatio-Temporal Data. Noel Cressie, Christopher K. Wikle
ISBN: 0471692743,9780471692744 | 624 pages | 16 Mb
Statistics for Spatio-Temporal Data Noel Cressie, Christopher K. Wikle
Stochastic processes and applied probability. Statistics for Spatio-Temporal Data (Wiley Desktop Editions) by Noel Cressie (Author), Christopher K. High-Dimensional Statistical Inference; Spatio-Temporal Data Applications; Computational Algorithms for High-Dimensional Data; Genomic Applications. There are many visual methods used to identify patterns in space and time. Epidemiology and Infection, 140 (9), 1663-1677. The main idea of GEOSTAT is to promote various aspects of statistical analysis of spatial and spatio-temporal data using open source / free GIS tools: R, SAGA GIS, GRASS GIS, FWTools, Google Earth and similar. (This article was first published on Intelligent Trading, and kindly contributed to R-bloggers). Their analysis, “Unique in the Crowd: the privacy bounds of human mobility” showed that data from just four, randomly chosen “spatio-temporal points” (for example, mobile device pings to carrier antennas) was enough to uniquely identify 95% of the individuals, Using a complex mathematical and statistical analysis of that data, the researchers discovered that it is possible to find one formula to express what they call the “uniqueness of human mobility”: e 5 a 2 (nh). Bayesian model selection and model averaging. Network inference for protein microarray data. It is an extensive revision of the author's earlier book,. R package: Interventional inference for Dynamic Bayesian The spatial and temporal determinants of campylobacteriosis notifications in New Zealand, 2001–2007. Inference for stochastic processes. The main goal of the project is to combine spatio-temporal models for pollution and health data into a single large hierarchical Bayesian model. (eds.) Spatio-Temporal Databases Flexible Querying.