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We construct an adaptive independent Metropolis-Hastings sampler that uses amixture of normals as a proposal distribution. To take full advantage of thepotential of adaptive sampling our algorithm updates the mixture of normalsfrequently, starting early in the chain. The algorithm is built for speed andreliability and its sampling performance is evaluated with real and simulatedexamples. Our article outlines conditions for adaptive sampling to hold andgives a readily accessible proof that under these conditions the samplingscheme generates iterates that converge to the target distribution.

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