Error bounds for Approximations of Markov chains used in Bayesian Sampling
暂无分享,去创建一个
[1] Richard L. Tweedie,et al. Markov Chains and Stochastic Stability , 1993, Communications and Control Engineering Series.
[2] Pierre Alquier,et al. Noisy Monte Carlo: convergence of Markov chains with approximate transition kernels , 2014, Statistics and Computing.
[3] Jonathan C. Mattingly,et al. Coupling and Decoupling to bound an approximating Markov Chain , 2017 .
[4] H. Haario,et al. An adaptive Metropolis algorithm , 2001 .
[5] S. Meyn,et al. Spectral theory and limit theorems for geometrically ergodic Markov processes , 2002, math/0209200.
[6] Max Welling,et al. Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget , 2013, ICML 2014.
[7] A. Stuart,et al. Spectral gaps for a Metropolis–Hastings algorithm in infinite dimensions , 2011, 1112.1392.
[8] N. Pillai,et al. Ergodicity of Approximate MCMC Chains with Applications to Large Data Sets , 2014, 1405.0182.
[9] Yee Whye Teh,et al. Bayesian Learning via Stochastic Gradient Langevin Dynamics , 2011, ICML.
[10] S. Meyn,et al. Geometric ergodicity and the spectral gap of non-reversible Markov chains , 2009, 0906.5322.
[11] Arnaud Doucet,et al. On Markov chain Monte Carlo methods for tall data , 2015, J. Mach. Learn. Res..
[12] Jonathan C. Mattingly,et al. Yet Another Look at Harris’ Ergodic Theorem for Markov Chains , 2008, 0810.2777.
[13] Sean P. Meyn,et al. A Liapounov bound for solutions of the Poisson equation , 1996 .
[14] Jonathan C. Mattingly,et al. Ergodicity of the 2D Navier-Stokes equations with degenerate stochastic forcing , 2004, math/0406087.
[15] J. Hobert,et al. Convergence rates and asymptotic standard errors for Markov chain Monte Carlo algorithms for Bayesian probit regression , 2007 .
[16] Andrew M. Stuart,et al. Convergence of Numerical Time-Averaging and Stationary Measures via Poisson Equations , 2009, SIAM J. Numer. Anal..
[17] R. Tweedie,et al. Rates of convergence of the Hastings and Metropolis algorithms , 1996 .
[18] Arnaud Doucet,et al. Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach , 2014, ICML.
[19] Jonathan C. Mattingly,et al. Asymptotic coupling and a general form of Harris’ theorem with applications to stochastic delay equations , 2009, 0902.4495.
[20] A. Gelfand,et al. Gaussian predictive process models for large spatial data sets , 2008, Journal of the Royal Statistical Society. Series B, Statistical methodology.
[21] S. F. Jarner,et al. Geometric ergodicity of Metropolis algorithms , 2000 .
[22] Aaron Smith,et al. MCMC for Imbalanced Categorical Data , 2016, Journal of the American Statistical Association.
[23] D. Rudolf,et al. Perturbation theory for Markov chains via Wasserstein distance , 2015, Bernoulli.
[24] D. Vere-Jones. Markov Chains , 1972, Nature.
[25] A. Y. Mitrophanov,et al. Sensitivity and convergence of uniformly ergodic Markov chains , 2005 .
[26] Tianqi Chen,et al. Stochastic Gradient Hamiltonian Monte Carlo , 2014, ICML.
[27] J. Rosenthal,et al. Optimal scaling for various Metropolis-Hastings algorithms , 2001 .