Statistics for Spatio-Temporal Data Noel Cressie, Christopher K. Wikle
Publisher: Wiley

Competitive applicants will possess a background in Bayesian statistical modeling, especially spatial/spatio-temporal modeling, state space modeling, or data assimilation. The main task will be the development and evaluation of dynamic visualisation methods for spatio-temporal data by combining techniques of computer graphics and statistical analysis. The health data (and even ecological data) that I analyze. In this paper the set of equations contained in the seNorge model code is described and a~thorough spatiotemporal statistical evaluation of the model performance in 1957–2011 is made using the two major sets of extensive in-situ snow measurements that exist for Norway. Clearly this was The session is titled An Overview of Models and Methods for Spatio-temporal Data Analysis, and is to be presented by Jim Zidek of the University of British Columbia. The seNorge model provides a relatively simple, not very data-demanding, yet still process-based method to construct snow maps of high spatiotemporal resolution. There are many visual methods used to identify patterns in space and time. In particular, the workshop aims at integrating recent results from existing fields such as data mining, statistics, machine learning and relational databases to discuss and introduce new algorithmic foundations and representation formalisms in pattern discovery. Complex patterns from text/hypertext data, networks and graphs, event or log data, biological data, spatio-temporal data, sensor data and streams, and so on. Following lunch I sat in on a 90 minute discussion that was panelled by five statistics educators with more than 200 years of teaching experience between them. A condition-of-the-artwork presentation of spatio-temporal processes, bridging vintage ideas with modern hierarchical statistical modeling principles and the most current computational approaches. (This article was first published on Intelligent Trading, and kindly contributed to R-bloggers).