General Methods for Monitoring Convergence of Iterative Simulations

Stephen P. Brooks; Andrew Gelman · Journal of Computational and Graphical Statistics · 1998

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Stephen P. Brooks; Andrew Gelman

Editore

Journal of Computational and Graphical Statistics

Anno

1998

Lingua

Inglés

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Abstract We generalize the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances of multiple chains, in order to obtain a family of tests for convergence. We review methods of inference from simulations in order to develop convergence-monitoring summaries that are relevant for the purposes for which the simulations are used. We recommend applying a battery of tests for mixing based on the comparison of inferences from individual sequences and from the mixture of sequences. Finally, we discuss multivariate analogues, for assessing convergence of several parameters simultaneously.

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APA 7

Brooks, S. P. & Gelman, A. (1998). General Methods for Monitoring Convergence of Iterative Simulations. https://doi.org/10.1080/10618600.1998.10474787

MLA

Brooks, Stephen P, and Andrew Gelman. "General Methods for Monitoring Convergence of Iterative Simulations." 1998. https://doi.org/10.1080/10618600.1998.10474787.

Chicago

Brooks, Stephen P. and Andrew Gelman. 1998. "General Methods for Monitoring Convergence of Iterative Simulations.". https://doi.org/10.1080/10618600.1998.10474787.

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Brooks, S. P. and Gelman, A. 1998, General Methods for Monitoring Convergence of Iterative Simulations, Journal of Computational and Graphical Statistics, available at: https://doi.org/10.1080/10618600.1998.10474787 [Accessed 6 Aug. 2026].

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Titolo: General Methods for Monitoring Convergence of Iterative Simulations

Autore / collaboratori: Stephen P. Brooks; Andrew Gelman

Editore: Journal of Computational and Graphical Statistics

Anno di pubblicazione: 1998

Lingua: Inglés

Soggetti

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Convergence (economics); Computer science; Inference; Mixing (physics); Algorithm; Mathematical optimization; Mathematics; Artificial intelligence; Quantum mechanics; Physics; Economics; Economic growth