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Markov Chain Monte Carlo Analysis

  • Dieter Gstach
Part of the Applied Optimization book series (APOP, volume 61)

Abstract

In the following I will give a very brief overview on the background of Markov chain Monte Carlo methodology to the extent, necessary to understand the idea behind the mechanics of the present application. The interested reader will find many textbooks covering the theory in detail, for example Gilks et al., 1996b or Gamerman, 1997. Note also that the present application is formulated in a Bayesian framework, while Markov chain Monte Carlo applications are not restricted at all to Bayesian type of analysis.

Keywords

Markov Chain Prior Distribution Direct Sampling Proposal Distribution Acceptance Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • Dieter Gstach
    • 1
  1. 1.Vienna University of EconomicsAustria

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