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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\bf MONTE CARLO MARKOV CHAINS IN SCIENTIFIC COMPUTING}
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\centerline{3:15 p.m., Thurday, October 19, 2000}
\centerline{Sequoia Hall Rm. 200}

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\centerline{\sl David Aldous}
\centerline{\sl U.C. Berkeley, Statistics}
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\centerline{\bf Multiple-Try Metropolis}
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I shall describe a new variant of the Metropolis algorithm, introduced by
Liu - Liang - Wong (JASA, to appear).  One interpretation of the effect of
Multiple-Try Metropolis is as an interpolation between Metropolis steps and
Gibbs (or hot-and-run) steps, in contexts where sampling from a
one-dimensional distribution (required by the latter schemes) is hard to
implement.

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