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Markov chain Monte Carlo (MCMC) algorithms are used to estimate features of interest of a distribution.
Bounds for the moments of linear and quadratic forms in independent variables
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Almost Sure Invariance Principles for Partial Sums of Weakly Dependent Random Variables
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Probability Theory
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Strong Approximations in Probability and Statistics
Csörgő, M. and Révész, P. (1981) · 1981
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Almost sure invariance principles for weakly dependent vector-valued random variables
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Strong approximation of extended renewal processes
Horvath, L. (1984) · 1984
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On strong invariance principles under dependence assumptions
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Extensions of results of Komlós, Major, and Tusnády to the multivariate case
Einmahl, U. (1989) · 1989
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Non-linear time series and Markov chains
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Heteroskedasticity and autocorrelation consistent covariance matrix estimation
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Strong consistency and other properties of the spectral variance estimator
Damerdji, H. (1991) · 1991
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Practical Markov chain Monte Carlo (with discussion)
Geyer, C. J. (1992) · 1992
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The asymptotic validity of sequential stopping rules for stochastic simulations
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Markov chains for exploring posterior distributions (with discussion)
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Regeneration in Markov chain samplers
Mykland, P., Tierney, L., and Yu, B. (1995) · 1995
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Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms
Roberts, G. O. and Tweedie, R. L. (1996) · 1996
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Analysis of the Gibbs sampler for a model related to James-Stein estimators
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Geometric ergodicity of Gibbs and block Gibbs samplers for a hierarchical random effects model
Hobert, J. P. and Geyer, C. J. (1998) · 1998
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Markov chain Monte Carlo in practice: a roundtable discussion
Kass, R. E., Carlin, B. P., Gelman, A., and Neal, R. M. (1998) · 1998
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Zaitsev, A. Y. (1998) · 1998
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Petrone, S., Roberts, G. O., and Rosenthal, J. S. (1999) · 1999
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Convergence of slice sampler Markov chains
Roberts, G. O. and Rosenthal, J. S. (1999) · 1999
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Liu, J. S. (2008) · 2008
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Meyn, S. P. and Tweedie, R. L. (2009) · 2009
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Estimation of Bayes factors in a class of hierarchical random effects models using a geometrically ergodic MCMC algorithm
Doss, H. and Hobert, J. P. (2010) · 2010
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Batch means and spectral variance estimators in Markov chain Monte Carlo
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Kernel estimators of asymptotic variance for adaptive Markov chain Monte Carlo
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Monte Carlo error estimation for multivariate Markov chains
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Honest exploration of intractable probability distributions via Markov chain Monte Carlo
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Variable transformation to obtain geometric ergodicity in the random-walk Metropolis algorithm
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Markov chain Monte Carlo estimation of quantiles
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Convergence of conditional Metropolis-Hastings samplers
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