Iterative Markov chain Monte Carlo computation of reference priors and minimax risk

  • Authors:

  • John Lafferty;Larry Wasserman

  • Affiliations:

  • School of Computer Science, Carnegie Mellon University, Pittsburgh, PA;Department of Statistics, Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
  • Year:
  • 2001

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Abstract

We present an iterative Markov chain Monte Carlo algorithm for computing reference priors and minimax risk for general parametric families. Our approach uses MCMC techniques based on the Blahut-Arimoto algorithm for computing channel capacity in information theory. We give a statistical analysis of the algorithm, bounding the number of samples required for the stochastic algorithm to closely approximate the deterministic algorithm in each iteration. Simulations are presented for several examples from exponential families. Although we focus on applications to reference priors and minimax risk, the methods and analysis we develop are applicable to a much broader class of optimization problems and iterative algorithms.