Ordering and Improving the Performance of Monte Carlo Markov Chains
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[1] M. Antonietta,et al. Stationary preserving and efficiency increasing probability mass transfer made possible , 2003 .
[2] L. Tierney,et al. Efficiency and Convergence Properties of Slice Samplers , 2002 .
[3] Julian Besag,et al. Markov Chain Monte Carlo for Statistical Inference , 2002 .
[4] P. Green,et al. Delayed rejection in reversible jump Metropolis–Hastings , 2001 .
[5] G. Roberts,et al. Approximate Predetermined Convergence Properties of the Gibbs Sampler , 2001 .
[6] H. Haario,et al. An adaptive Metropolis algorithm , 2001 .
[7] Joseph B. Kadane,et al. Practical Regeneration for Markov Chain Monte Carlo Simulation , 2001 .
[8] P. Diaconis,et al. A geometric interpretation of the Metropolis-Hastings algorithm , 2001 .
[9] A. Mira. On Metropolis-Hastings algorithms with delayed rejection , 2001 .
[10] Radford M. Neal,et al. ANALYSIS OF A NONREVERSIBLE MARKOV CHAIN SAMPLER , 2000 .
[11] W. Wefelmeyer,et al. Markov chain Monte Carlo and Rao–Blackwellization , 2000 .
[12] C. Geyer. On Non-reversible Markov Chains , 2000 .
[13] Heikki Haario,et al. Adaptive proposal distribution for random walk Metropolis algorithm , 1999, Comput. Stat..
[14] L Tierney,et al. Some adaptive monte carlo methods for Bayesian inference. , 1999, Statistics in medicine.
[15] Gareth O. Roberts,et al. On convergence of the EM algorithmand the Gibbs sampler , 1999, Stat. Comput..
[16] Reversible Markov chains and optimality of symmetrized empirical estimators , 1999 .
[17] Didier Chauveau,et al. Improving convergence of the Hastings-MetropolisAlgorithm with a learning proposal , 1999 .
[18] Antonietta Mira,et al. Ordering Monte Carlo Markov Chains , 1999 .
[19] G. Roberts,et al. Adaptive Markov Chain Monte Carlo through Regeneration , 1998 .
[20] L. Tierney. A note on Metropolis-Hastings kernels for general state spaces , 1998 .
[21] Radford M. Neal. Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation , 1995, Learning in Graphical Models.
[22] Lars Holden,et al. Adaptive Chains , 1998 .
[23] G. Roberts,et al. Updating Schemes, Correlation Structure, Blocking and Parameterization for the Gibbs Sampler , 1997 .
[24] Jun S. Liu. Peskun's theorem and a modified discrete-state Gibbs sampler , 1996 .
[25] W. Wefelmeyer,et al. Outperforming the Gibbs sampler empirical estimator for nearest-neighbor random fields , 1996 .
[26] Jun S. Liu,et al. Metropolized independent sampling with comparisons to rejection sampling and importance sampling , 1996, Stat. Comput..
[27] G. Casella,et al. Rao-Blackwellisation of sampling schemes , 1996 .
[28] P. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .
[29] Sylvia Richardson,et al. Markov chain concepts related to sampling algorithms , 1995 .
[30] Priscilla E. Greenwood,et al. Efficiency of Empirical Estimators for Markov Chains , 1995 .
[31] Jun S. Liu,et al. Covariance Structure and Convergence Rate of the Gibbs Sampler with Various Scans , 1995 .
[32] L. Tierney. Markov Chains for Exploring Posterior Distributions , 1994 .
[33] C. Hwang,et al. Convergence rates of the Gibbs sampler, the Metropolis algorithm and other single-site updating dynamics , 1993 .
[34] J. Besag,et al. Spatial Statistics and Bayesian Computation , 1993 .
[35] C. Hwang,et al. Optimal Spectral Structure of Reversible Stochastic Matrices, Monte Carlo Methods and the Simulation of Markov Random Fields , 1992 .
[36] Adrian F. M. Smith,et al. Sampling-Based Approaches to Calculating Marginal Densities , 1990 .
[37] P. Peskun,et al. Optimum Monte-Carlo sampling using Markov chains , 1973 .
[38] Richard Bellman,et al. Introduction to Matrix Analysis , 1972 .
[39] S. Sherman,et al. Monotone and convex operator functions , 1955 .
[40] Karl Löwner. Über monotone Matrixfunktionen , 1934 .