Adaptive Independent Metropolis-Hastings by Fast Estimation of Mixtures of Normals

35 Pages Posted: 12 Jan 2008

See all articles by Paolo Giordani

Paolo Giordani

Norwegian Business School

Robert Kohn

University of New South Wales - School of Economics and School of Banking and Finance

Date Written: January 12, 2008

Abstract

We construct an adaptive independent Metropolis-Hastings sampler that uses a mixture of normals as a proposal distribution. To take full advantage of the potential of adaptive sampling our algorithm updates the mixture of normals frequently, starting early in the chain. The algorithm is built for speed and reliability and its sampling performance is evaluated with real and simulated examples.

Our article outlines conditions for adaptive sampling to hold and gives a readily accessible proof that under these conditions the sampling scheme generates iterates that converge to the target distribution.

Keywords: Clustering, Gibbs sampling, Markov chain, Monte Carlo

JEL Classification: C11, C14, C15

Suggested Citation

Giordani, Paolo and Kohn, Robert, Adaptive Independent Metropolis-Hastings by Fast Estimation of Mixtures of Normals (January 12, 2008). Available at SSRN: https://ssrn.com/abstract=1082955 or http://dx.doi.org/10.2139/ssrn.1082955

Paolo Giordani

Norwegian Business School ( email )

Nydalsveien 37
Oslo, 0442
Norway

Robert Kohn (Contact Author)

University of New South Wales - School of Economics and School of Banking and Finance ( email )

Australian School of Business
Sydney NSW 2052, ACT 2600
Australia
+61 2 9385 2150 (Phone)

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