Bayesian Geophysical, Spatial and Temporal Statistics

  • Ming-Hui Chen
  • Dipak K. Dey
  • Peter Müller
  • Dongchu Sun
  • Keying Ye
Chapter

Abstract

Spatio-temporal models give rise to many challenging research frontiers in Bayesian analysis. One simple reason is that the spatial and/or time series nature of the data implies complicated dependence structures that require modeling and lead to often challenging inference problems. The power of the Bayesian approach comes to bear especially when inference is desired on aspects of the model that are removed from the data by various levels in the hierarchical model. In this chapter we discuss two examples of such problems and also review the use of non-informative priors in spatial models.

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Copyright information

© Springer New York 2010

Authors and Affiliations

  • Ming-Hui Chen
    • 1
  • Dipak K. Dey
    • 1
  • Peter Müller
    • 2
  • Dongchu Sun
    • 3
  • Keying Ye
    • 4
  1. 1.Department of StatisticsUniversity of ConnecticutStorrsUSA
  2. 2.Department of BiostatisticsThe University of Texas, M. D. Anderson Cancer CenterHoustonUSA
  3. 3.Department of StatisticsUniversity of Missouri-ColumbiaColumbiaUSA
  4. 4.Department of Management Science and Statistics, College of BusinessUniversity of Texas at San AntonioSan AntonioUSA

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