  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.
