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Bayesian Inference in Political Science, Finance, and Marketing Research

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

Many current research challenges in Bayesian analysis arise in applications. A beauty of the Bayesian approach is that it facilitates principled inference in essentially any well-specified probability model or decision problem. In principle one could consider arbitrarily complicated priors, probability models and decision problems. However, not even the most creatively convoluted mind could dream up the complexities, wrinkles and complications that arise in actual applications. In this chapter we discuss typical examples of such challenges, ranging from prior constructions in political science applications, to model based data transformation for the display of multivariate marketing data, to challenging posterior simulation for state space models in finance and to expected utility maximization for portfolio selection.

Suggested Citation

  • Ming-Hui Chen & Dipak K. Dey & Peter Müller & Dongchu Sun & Keying Ye, 2010. "Bayesian Inference in Political Science, Finance, and Marketing Research," 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 377-417, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4419-6944-6_11
    DOI: 10.1007/978-1-4419-6944-6_11
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