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Posterior Simulation and Monte Carlo Methods

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

A beauty of the Bayesian approach is the principled nature of inference. There is a gold standard of how to proceed, and the basic principle is easily explained. However, the actual implementation often gives rise to many challenges. One of the challenges that remains an important research frontier of Bayesian inference is the problem of numerically evaluating the desired posterior summaries. In this chapter we review some related specific research problems.

Suggested Citation

  • Ming-Hui Chen & Dipak K. Dey & Peter Müller & Dongchu Sun & Keying Ye, 2010. "Posterior Simulation and Monte Carlo Methods," 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 513-553, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4419-6944-6_14
    DOI: 10.1007/978-1-4419-6944-6_14
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