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Weak Poincaré inequalities for two-component Deterministic-scan Metropolis-within-Gibbs samplers
Using the framework of weak Poincaré inequalities, we analyze the convergence properties of deterministic-scan Metropolis-within-Gibbs samplers, an important class of Markov chain Monte Carlo algorithms. Our analysis applies to nonreversible Markov chains and yields explicit (subgeometric) convergen
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