Hierarchical Modeling for Spatially-referenced Data with Applications to Environmental Sciences and Public Health
NESS 2009, University of Connecticut, Storrs, April, 24th 2009
Sudipto Banerjee (University of Minnesota)
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 software for their practical implementation with special emphasis on spatial modelling.
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 the two softwares we will use:
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
1:00pm - 1:45pm: LECTURE 1
1:45pm - 2:30pm: COMPUTING NOTES
2:30pm - 2:45pm: Afternoon Break
2:45pm - 3:20pm: LECTURE 2
3:20pm - 3:45pm: COMPUTING
3:45pm - 4:00pm: LECTURE 3
4:00pm - 4:20pm: COMPUTING
Special Topics
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4:20pm - 4:40pm: LECTURE 4
4:40pm - 5:00pm: LECTURE 5