The Data Augmentation Algorithm: Theory and Methodology
暂无分享,去创建一个
[1] R. Tweedie,et al. Geometric L 2 and L 1 convergence are equivalent for reversible Markov chains , 2001, Journal of Applied Probability.
[2] Charles J. Geyer,et al. Practical Markov Chain Monte Carlo , 1992 .
[3] S. Meyn,et al. Computable Bounds for Geometric Convergence Rates of Markov Chains , 1994 .
[4] Jeffrey S. Rosenthal,et al. Asymptotic Variance and Convergence Rates of Nearly-Periodic Markov Chain Monte Carlo Algorithms , 2003 .
[5] E. Nummelin. General irreducible Markov chains and non-negative operators: Notes and comments , 1984 .
[6] Xiao-Li Meng,et al. The Art of Data Augmentation , 2001 .
[7] J. Rosenthal,et al. General state space Markov chains and MCMC algorithms , 2004, math/0404033.
[8] Michael W Deem,et al. Parallel tempering: theory, applications, and new perspectives. , 2005, Physical chemistry chemical physics : PCCP.
[9] Jun S. Liu,et al. Covariance Structure and Convergence Rate of the Gibbs Sampler with Various Scans , 1995 .
[10] Gareth O. Roberts,et al. Markov Chains and De‐initializing Processes , 2001 .
[11] J. Hobert,et al. Convergence rates and asymptotic standard errors for Markov chain Monte Carlo algorithms for Bayesian probit regression , 2007 .
[12] Galin L. Jones,et al. Honest Exploration of Intractable Probability Distributions via Markov Chain Monte Carlo , 2001 .
[13] Galin L. Jones,et al. Fixed-Width Output Analysis for Markov Chain Monte Carlo , 2006, math/0601446.
[14] Wang,et al. Nonuniversal critical dynamics in Monte Carlo simulations. , 1987, Physical review letters.
[15] Murali Haran,et al. Markov chain Monte Carlo: Can we trust the third significant figure? , 2007, math/0703746.
[16] J. Hobert,et al. Block Gibbs Sampling for Bayesian Random Effects Models With Improper Priors: Convergence and Regeneration , 2009 .
[17] Sheldon M. Ross,et al. Stochastic Processes , 2018, Gauge Integral Structures for Stochastic Calculus and Quantum Electrodynamics.
[18] J. Geweke,et al. Bayesian Inference in Econometric Models Using Monte Carlo Integration , 1989 .
[19] Xiao-Li Meng,et al. Seeking efficient data augmentation schemes via conditional and marginal augmentation , 1999 .
[20] J. Rosenthal. Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo , 1995 .
[21] Jun S. Liu,et al. Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes , 1994 .
[22] J. Hobert,et al. Geometric Ergodicity of van Dyk and Meng's Algorithm for the Multivariate Student's t Model , 2004 .
[23] J. Hobert,et al. A theoretical comparison of the data augmentation, marginal augmentation and PX-DA algorithms , 2008, 0804.0671.
[24] Adrian F. M. Smith,et al. Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms , 1994 .
[25] C. Geyer,et al. Discussion: Markov Chains for Exploring Posterior Distributions , 1994 .
[26] Richard L. Tweedie,et al. Markov Chains and Stochastic Stability , 1993, Communications and Control Engineering Series.
[27] James Allen Fill,et al. Extension of Fill's perfect rejection sampling algorithm to general chains (Extended abstract) , 2000 .
[28] C. Robert. Simulation of truncated normal variables , 2009, 0907.4010.
[29] Bin Yu,et al. Regeneration in Markov chain samplers , 1995 .
[30] S. Chib,et al. Bayesian analysis of binary and polychotomous response data , 1993 .
[31] P. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .
[32] James P. Hobert,et al. Norm Comparisons for Data Augmentation , 2007 .
[33] P. Diaconis,et al. Geometric Bounds for Eigenvalues of Markov Chains , 1991 .
[34] M. L. Eaton. Group invariance applications in statistics , 1989 .
[35] M. Steel,et al. Multivariate Student -t Regression Models : Pitfalls and Inference , 1999 .
[36] L. Tierney. Markov Chains for Exploring Posterior Distributions , 1994 .
[37] J. Rosenthal,et al. Geometric Ergodicity and Hybrid Markov Chains , 1997 .
[38] P. Diaconis,et al. Gibbs sampling, exponential families and orthogonal polynomials , 2008, 0808.3852.
[39] J. Rosenthal,et al. Harris recurrence of Metropolis-within-Gibbs and trans-dimensional Markov chains , 2006, math/0702412.
[40] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[41] C. D. Gelatt,et al. Optimization by Simulated Annealing , 1983, Science.
[42] Galin L. Jones,et al. On the applicability of regenerative simulation in Markov chain Monte Carlo , 2002 .
[43] Ming-Hui Chen,et al. Propriety of posterior distribution for dichotomous quantal response models , 2000 .
[44] G. Torrie,et al. Nonphysical sampling distributions in Monte Carlo free-energy estimation: Umbrella sampling , 1977 .
[45] S. Walker. Invited comment on the paper "Slice Sampling" by Radford Neal , 2003 .
[46] Ruitao Liu,et al. When is Eaton’s Markov chain irreducible? , 2007 .
[47] G. Parisi,et al. Simulated tempering: a new Monte Carlo scheme , 1992, hep-lat/9205018.
[48] Jun S. Liu,et al. Parameter Expansion for Data Augmentation , 1999 .