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Bayesian Geophysical, Spatial and Temporal Statistics

In: Frontiers of Statistical Decision Making and Bayesian Analysis

Author

Listed:
  • Ming-Hui Chen

    (University of Connecticut, Department of Statistics)

  • Dipak K. Dey

    (University of Connecticut, Department of Statistics)

  • Peter Müller

    (The University of Texas, M. D. Anderson Cancer Center, Department of Biostatistics)

  • Dongchu Sun

    (University of Missouri-Columbia, Department of Statistics)

  • Keying Ye

    (University of Texas at San Antonio, Department of Management Science and Statistics, College of Business)

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.

Suggested Citation

  • Ming-Hui Chen & Dipak K. Dey & Peter Müller & Dongchu Sun & Keying Ye, 2010. "Bayesian Geophysical, Spatial and Temporal Statistics," Springer Books, in: Ming-Hui Chen & Peter Müller & Dongchu Sun & Keying Ye & Dipak K. Dey (ed.), Frontiers of Statistical Decision Making and Bayesian Analysis, chapter 0, pages 467-511, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4419-6944-6_13
    DOI: 10.1007/978-1-4419-6944-6_13
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