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• W2113132439 abstract "This paper analyzes the Gibbs sampler applied to a standard variance component model, and considers the question of how many iterations are required for convergence. It is proved that for $K$ location parameters, with $J$ observations each, the number of iterations required for convergence (for large $K$ and $J$) is a constant times $(1 + log K/log J)$. This is one of the first rigorous, a priori results about time to convergence for the Gibbs sampler. A quantitative version of the theory of Harris recurrence (for Markov chains) is developed and applied." @default.
• W2113132439 created "2016-06-24" @default.
• W2113132439 creator A5077911233 @default.
• W2113132439 date "1995-06-01" @default.
• W2113132439 modified "2023-10-13" @default.
• W2113132439 title "Rates of Convergence for Gibbs Sampling for Variance Component Models" @default.
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• W2113132439 doi "https://doi.org/10.1214/aos/1176324619" @default.
• W2113132439 hasPublicationYear "1995" @default.
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