Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convergence Rates of Two-Component MCMC Samplers   Component-wise MCMC algorithms, including Gibbs and conditional
Metropolis-Hastings samplers, are commonly used for sampling from multivariate
probability distributions. A long-standing question regarding Gibbs algorithms
is whether a deterministic-scan (systematic-scan) sampler converges faster than
its random-scan counterpart. We answer this question when the samplers involve
two components by establishing an exact quantitative relationship between the
$L^2$ convergence rates of the two samplers. The relationship shows that the
deterministic-scan sampler converges faster. We also establish qualitative
relations among the convergence rates of two-component Gibbs samplers and some
conditional Metropolis-Hastings variants. For instance, it is shown that if
some two-component conditional Metropolis-Hastings samplers are geometrically
ergodic, then so are the associated Gibbs samplers.

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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: physics
23
The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: adap-org
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The categories of this paper are: plasm-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-th
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The categories of this paper are: econ
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph