Convergence rate of Markov chain methods for genomic motif discovery
暂无分享,去创建一个
[1] J. Doob. Stochastic processes , 1953 .
[2] Solomon Kullback,et al. Information Theory and Statistics , 1960 .
[3] R. Berk,et al. Limiting Behavior of Posterior Distributions when the Model is Incorrect , 1966 .
[4] P. Peskun,et al. Optimum Monte-Carlo sampling using Markov chains , 1973 .
[5] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] John Geweke,et al. Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments , 1991 .
[7] P. Diaconis,et al. Geometric Bounds for Eigenvalues of Markov Chains , 1991 .
[8] Alistair Sinclair,et al. Improved Bounds for Mixing Rates of Markov Chains and Multicommodity Flow , 1992, Combinatorics, Probability and Computing.
[9] D. Rubin,et al. Inference from Iterative Simulation Using Multiple Sequences , 1992 .
[10] P. Diaconis,et al. COMPARISON THEOREMS FOR REVERSIBLE MARKOV CHAINS , 1993 .
[11] Jun S. Liu,et al. Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. , 1993, Science.
[12] J. Rosenthal. Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo , 1995 .
[13] Jun S. Liu,et al. The Collapsed Gibbs Sampler in Bayesian Computations with Applications to a Gene Regulation Problem , 1994 .
[14] Jun S. Liu,et al. Bayesian Models for Multiple Local Sequence Alignment and Gibbs Sampling Strategies , 1995 .
[15] Jun S. Liu,et al. Covariance Structure and Convergence Rate of the Gibbs Sampler with Various Scans , 1995 .
[16] Jun S. Liu,et al. Gibbs motif sampling: Detection of bacterial outer membrane protein repeats , 1995, Protein science : a publication of the Protein Society.
[17] Jeffrey S. Rosenthal,et al. Analysis of the Gibbs Sampler for a Model Related to James-stein Estimators , 2007 .
[18] P. Diaconis,et al. LOGARITHMIC SOBOLEV INEQUALITIES FOR FINITE MARKOV CHAINS , 1996 .
[19] L. Tierney. A note on Metropolis-Hastings kernels for general state spaces , 1998 .
[20] G. Church,et al. Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation , 1998, Nature Biotechnology.
[21] Alan M. Frieze,et al. Torpid mixing of some Monte Carlo Markov chain algorithms in statistical physics , 1999, 40th Annual Symposium on Foundations of Computer Science (Cat. No.99CB37039).
[22] S. F. Jarner,et al. Geometric ergodicity of Metropolis algorithms , 2000 .
[23] S. Q. s3idChMn,et al. Evolutionary Monte Carlo: Applications to C_p Model Sampling and Change Point Problem , 2000 .
[24] Faming Liang,et al. EVOLUTIONARY MONTE CARLO: APPLICATIONS TO Cp MODEL SAMPLING AND CHANGE POINT PROBLEM , 2000 .
[25] Galin L. Jones,et al. Honest Exploration of Intractable Probability Distributions via Markov Chain Monte Carlo , 2001 .
[26] G. Roberts,et al. Approximate Predetermined Convergence Properties of the Gibbs Sampler , 2001 .
[27] Antonietta Mira,et al. Ordering and Improving the Performance of Monte Carlo Markov Chains , 2001 .
[28] Douglas L. Brutlag,et al. BioProspector: Discovering Conserved DNA Motifs in Upstream Regulatory Regions of Co-Expressed Genes , 2000, Pacific Symposium on Biocomputing.
[29] D. Randall,et al. Markov chain decomposition for convergence rate analysis , 2002 .
[30] P. Moral,et al. Sequential Monte Carlo samplers , 2002, cond-mat/0212648.
[31] P. Green,et al. Hidden Markov Models and Disease Mapping , 2002 .
[32] Neal Madras,et al. On the swapping algorithm , 2003, Random Struct. Algorithms.
[33] G. Fort,et al. On the geometric ergodicity of hybrid samplers , 2003, Journal of Applied Probability.
[34] J. Rosenthal,et al. General state space Markov chains and MCMC algorithms , 2004, math/0404033.
[35] Shane T. Jensen,et al. Computational Discovery of Gene Regulatory Binding Motifs: A Bayesian Perspective , 2004 .
[36] Dana Randall,et al. Torpid mixing of simulated tempering on the Potts model , 2004, SODA '04.
[37] Bonnie Berger,et al. Methods in Comparative Genomics: Genome Correspondence, Gene Identification and Regulatory Motif Discovery , 2004, J. Comput. Biol..
[38] Galin L. Jones,et al. Sufficient burn-in for Gibbs samplers for a hierarchical random effects model , 2004, math/0406454.
[39] Elchanan Mossel,et al. Limitations of Markov chain Monte Carlo algorithms for Bayesian inference of phylogeny , 2005, The Annals of Applied Probability.
[40] S. Kou,et al. Equi-energy sampler with applications in statistical inference and statistical mechanics , 2005, math/0507080.
[41] A. Belloni,et al. On the Computational Complexity of MCMC-Based Estimators in Large Samples , 2007 .
[42] Alicia A. Johnson,et al. Gibbs sampling for a Bayesian hierarchical general linear model , 2007 .
[43] M. West,et al. Shotgun Stochastic Search for “Large p” Regression , 2007 .
[44] Darren J. Wilkinson,et al. Discussion of Particle Markov chain Monte Carlo , 2008 .
[45] D. Woodard,et al. Conditions for Rapid and Torpid Mixing of Parallel and Simulated Tempering on Multimodal Distributions , 2009, 0906.2341.
[46] Chao Yang,et al. Learn From Thy Neighbor: Parallel-Chain and Regional Adaptive MCMC , 2009 .
[47] D. Woodard,et al. Sufficient Conditions for Torpid Mixing of Parallel and Simulated Tempering , 2009 .
[48] K. Kamatani. Local consistency of Markov chain Monte Carlo methods , 2010, 1012.0996.
[49] Weak consistency of Markov chain Monte Carlo methods , 2011, 1103.5679.
[50] M. Li,et al. Particle Markov chain Monte Carlo methods , 2015 .
[51] D. Woodard,et al. Conditions for Torpid Mixing of Parallel and Simulated Tempering on Multimodal Distributions , 2022 .