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Visualizations for Assessing Convergence and Mixing of MCMC

  • Jarkko Venna
  • Samuel Kaski
  • Jaakko Peltonen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2837)

Abstract

Bayesian inference often requires approximating the posterior distribution with Markov Chain Monte Carlo (MCMC) sampling. A central problem with MCMC is how to detect whether the simulation has converged. The samples come from the true posterior distribution only after convergence. A common solution is to start several simulations from different starting points, and measure overlap of the different chains. We point out that Linear Discriminant Analysis (LDA) minimizes the overlap measured by the usual multivariate overlap measure. Hence, LDA is a justified method for visualizing convergence. However, LDA makes restrictive assumptions about the distributions of the chains and their relationships. These restrictions can be relaxed by a recently introduced extension.

Keywords

Markov Chain Monte Carlo Linear Discriminant Analysis Markov Chain Monte Carlo Sampler Markov Chain Monte Carlo Chain Bayesian Data Analysis 
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-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jarkko Venna
    • 1
  • Samuel Kaski
    • 1
  • Jaakko Peltonen
    • 1
  1. 1.Neural Networks Research CentreHelsinki University of TechnologyHUTFinland

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