Summer School on Spatial Statistics 2009<
SAMSI 2009, Research Triangle Park, NC, Jul 28th - Aug 1st 2009
Sudipto Banerjee (U. Minnesota), Reinhard Furrer (U. Zurich), Doug Nychka (National Center for Atmospheric Research), and Stephen Sain (National Center for Atmospheric Research)
Bayesian methods enable the combining of information from similar and independent experiments and also allow the incorporation of prior information in statistical analysis. This segment of the course introduces Bayesian methods for spatial data analysis and some software for their practical implementation.
The following are useful text books for Bayesian statistics, modelling and hierarchical models:
Banerjee, S., Carlin, B.P. and Gelfand, A.E. (2004). Hierarchical Modeling and Analysis for Spatial Data. Publisher: CRC/Chapman and Hall.
Diggle, P.J. and Ribeiro Jr., P.J. (2007). Model-based Geostatistics. Publisher: Springer.
Waller, L. and Gotway, C. (2004). Applied Spatial Statistics for Public Health Data. Publishers: John Wiley and Sons.
Carlin, B.P. and Louis, T.A. (2000).Bayes and Empirical Bayes Methods for Data Analysis. Second Edition. Publisher: CRC/Chapman and Hall.
Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B. (2004). Bayesian Data Analysis. Second Edition. Publisher: CRC/Chapman and Hall.
Dalgaard, P. (2002). Introductory Statistics with R.
Faraway, J.J. (2005). Linear Models with R. Publisher: CRC/Chapman and Hall.
Lee, P. M. (2004). Bayesian Statistics Publisher: Hodder Arnold
Venables, W.N., Smith, D.M. and the R Development Core Team (2002). An Introduction to R: Revised and Updated.
The web sites for softwares:
WinBUGS or OpenBUGS
You can download the new registration key for WinBUGS from HERE. NOTE THAT YOU DO NOT REQUIRE ANY REGISTRATION KEY FOR OPENBUGS.
R.
Course Notes
2:45pm - 3:45pm: COMPUTING
3:45pm - 4:00pm: LECTURE 3
4:00pm - 4:30pm: COMPUTING
July 31st, Friday
Special Topic
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9:00am - 10:30am: LECTURE 4
10:30am - 12:00noon
Special Topics