Density estimation for the Metropolis-Hastings algorithm

Martin Sköld, G O Roberts

Research output: Contribution to journalArticlepeer-review

12 Citations (SciVal)

Abstract

Kernel density estimation is an important tool in visualizing posterior densities from Markov chain Monte Carlo output. It is well known that when smooth transition densities exist, the asymptotic properties of the estimator agree with those for independent data. In this paper, we show that because of the rejection step of the Metropolis-Hastings algorithm, this is no longer true and the asymptotic variance will depend on the probability of accepting a proposed move. We find an expression for this variance and apply the result to algorithms for automatic bandwidth selection.
Original languageEnglish
Pages (from-to)699-718
JournalScandinavian Journal of Statistics
Volume30
Issue number4
DOIs
Publication statusPublished - 2003

Subject classification (UKÄ)

  • Probability Theory and Statistics

Keywords

  • density estimation
  • Metropolis-Hastings algorithm

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