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Introduction to “Quantitative bounds of convergence for geometrically ergodic Markov Chain in the Wasserstein distance with application to the Metropolis adjusted Langevin algorithm” by A. Durmus, É. Moulines

机译：介绍了Wassersein距离的几何ergodic markov链条的定量范围与应用到Metropolis调整后的Langevin算法“，A. Durmus，é。机制

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Heikki Haario;
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年度 2014
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1. Introduction to 'Quantitative bounds of convergence for geometrically ergodic Markov Chain in the Wasserstein distance with application to the Metropolis adjusted Langevin algorithm' by A. Durmus, E. Moulines [J] . Heikki Haario Statistics and computing . 2015,第1期

机译：A. Durmus，E. Moulines所著的“ Wasserstein距离上的几何遍历马尔可夫链的收敛量化界及其在大都会调整的Langevin算法中的应用”简介
2. Quantitative bounds of convergence for geometrically ergodic Markov chain in the Wasserstein distance with application to the Metropolis Adjusted Langevin Algorithm [J] . Alain Durmus, Eric Moulines Statistics and computing . 2015,第1期

机译：Wasserstein距离上几何遍历马尔可夫链的收敛定量界及其在大都会调整的Langevin算法中的应用。
3. Exponential inequalities for unbounded functions of geometrically ergodic Markov chains: applications to quantitative error bounds for regenerative Metropolis algorithms [J] . Wintenberger Olivier Statistics . 2017,第1a3期

机译：几何遍历马尔可夫链的无穷函数的指数不等式：应用于再生Metropolis算法的定量误差范围
4. Applications of differential inequalities to bounding the rate of convergence for continuous-time Markov chains [C] . Alexander Zeifman, Yacov Satin, Ksenia Kiseleva, International Conference of Numerical Analysis and Applied Mathematics . 2019

机译：差分不等式对连续时间马尔可夫链的收敛速率的应用
5. Convergence of Markov chain Monte Carlo algorithms with applications to image restoration. [D] . Gibbs, Alison Lee. 2000

机译：马尔可夫链蒙特卡罗算法在图像复原中的收敛性。
6. Exponential inequalities for unbounded functions of geometrically ergodic Markov chains: applications to quantitative error bounds for regenerative Metropolis algorithms [O] . Olivier Wintenberger 2016

机译：几何ergodic Markov链条未绑定功能的指数不等式：对再生大都市算法定量误差界的应用
7. Convergence Bounds for Markov Chains and Applications to Sampling [R] . Gangolli, A. R. 1991

机译：马尔可夫链的收敛边界及其对采样的应用

Introduction to “Quantitative bounds of convergence for geometrically ergodic Markov Chain in the Wasserstein distance with application to the Metropolis adjusted Langevin algorithm” by A. Durmus, É. Moulines

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