The tamed unadjusted Langevin algorithm
暂无分享,去创建一个
[1] M. Ledoux,et al. Analysis and Geometry of Markov Diffusion Operators , 2013 .
[2] R. Tweedie,et al. Exponential convergence of Langevin distributions and their discrete approximations , 1996 .
[3] Y. Atchadé. An Adaptive Version for the Metropolis Adjusted Langevin Algorithm with a Truncated Drift , 2006 .
[4] S. Glotzer,et al. Time-course gait analysis of hemiparkinsonian rats following 6-hydroxydopamine lesion , 2004, Behavioural Brain Research.
[5] Lester W. Mackey,et al. Measuring Sample Quality with Diffusions , 2016, The Annals of Applied Probability.
[6] Michael I. Miller,et al. REPRESENTATIONS OF KNOWLEDGE IN COMPLEX SYSTEMS , 1994 .
[7] E. Vanden-Eijnden,et al. Pathwise accuracy and ergodicity of metropolized integrators for SDEs , 2009, 0905.4218.
[8] L. C. G. Rogers,et al. STOCHASTIC DIFFERENTIAL EQUATIONS AND DIFFUSION PROCESSES (North‐Holland Mathematical Library, 24) , 1982 .
[9] 池田 信行,et al. Stochastic differential equations and diffusion processes , 1981 .
[10] Gabriel Stoltz,et al. Partial differential equations and stochastic methods in molecular dynamics* , 2016, Acta Numerica.
[11] P. Kloeden,et al. Strong and weak divergence in finite time of Euler's method for stochastic differential equations with non-globally Lipschitz continuous coefficients , 2009, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[12] Jonathan C. Mattingly,et al. An adaptive Euler–Maruyama scheme for SDEs: convergence and stability , 2006, math/0601029.
[13] R. Douc,et al. Subgeometric rates of convergence of f-ergodic strong Markov processes , 2006, math/0605791.
[14] E. Pardoux,et al. On the Poisson Equation and Diffusion Approximation. I Dedicated to N. v. Krylov on His Sixtieth Birthday , 2001 .
[15] Cédric Villani,et al. Stability of optimal transport , 2009 .
[16] Chaman Kumar,et al. On Milstein approximations with varying coefficients: the case of super-linear diffusion coefficients , 2016, BIT Numerical Mathematics.
[17] S. Meyn,et al. Stability of Markovian processes III: Foster–Lyapunov criteria for continuous-time processes , 1993, Advances in Applied Probability.
[18] Andrew M. Stuart,et al. Convergence of Numerical Time-Averaging and Stationary Measures via Poisson Equations , 2009, SIAM J. Numer. Anal..
[19] M. Aschwanden. Statistics of Random Processes , 2021, Biomedical Measurement Systems and Data Science.
[20] Nando de Freitas,et al. An Introduction to MCMC for Machine Learning , 2004, Machine Learning.
[21] Andrew M. Stuart,et al. Strong Convergence of Euler-Type Methods for Nonlinear Stochastic Differential Equations , 2002, SIAM J. Numer. Anal..
[22] É. Moulines,et al. Non-asymptotic convergence analysis for the Unadjusted Langevin Algorithm , 2015, 1507.05021.
[23] M. Kopec. Weak backward error analysis for overdamped Langevin processes , 2013, 1310.2404.
[24] G. Parisi. Correlation functions and computer simulations (II) , 1981 .
[25] Djalil CHAFAÏ,et al. Central limit theorems for additive functionals of ergodic Markov diffusions processes , 2011, 1104.2198.
[26] G. Roberts,et al. Kinetic energy choice in Hamiltonian/hybrid Monte Carlo , 2017, Biometrika.
[27] G. Roberts,et al. MCMC Methods for Functions: ModifyingOld Algorithms to Make Them Faster , 2012, 1202.0709.
[28] A. Eberle. Couplings, distances and contractivity for diffusion processes revisited , 2013 .
[29] Xiaojie Wang,et al. The tamed Milstein method for commutative stochastic differential equations with non-globally Lipschitz continuous coefficients , 2011, 1102.0662.
[30] S. F. Jarner,et al. Geometric ergodicity of Metropolis algorithms , 2000 .
[31] Arnak S. Dalalyan,et al. Sparse Regression Learning by Aggregation and Langevin Monte-Carlo , 2009, COLT.
[32] P. Kloeden,et al. Strong convergence of an explicit numerical method for SDEs with nonglobally Lipschitz continuous coefficients , 2010, 1010.3756.
[33] Alain Durmus,et al. High-dimensional Bayesian inference via the unadjusted Langevin algorithm , 2016, Bernoulli.
[34] Xiongzhi Chen. Brownian Motion and Stochastic Calculus , 2008 .
[35] P. Bassanini,et al. Elliptic Partial Differential Equations of Second Order , 1997 .
[36] A. Veretennikov,et al. On the poisson equation and diffusion approximation 3 , 2001, math/0506596.
[37] Alʹbert Nikolaevich Shiri︠a︡ev,et al. Statistics of random processes , 1977 .
[38] E. Vanden-Eijnden,et al. Non-asymptotic mixing of the MALA algorithm , 2010, 1008.3514.
[39] A. Shiryayev,et al. Statistics of Random Processes I: General Theory , 1984 .
[40] Jonathan C. Mattingly,et al. Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise , 2002 .
[41] S. Sabanis. A note on tamed Euler approximations , 2013, 1303.5504.
[42] A. Dalalyan. Theoretical guarantees for approximate sampling from smooth and log‐concave densities , 2014, 1412.7392.
[43] D. W. Stroock,et al. Multidimensional Diffusion Processes , 1979 .
[44] Sean P. Meyn,et al. A Liapounov bound for solutions of the Poisson equation , 1996 .
[45] Chaman Kumar,et al. On tamed milstein schemes of SDEs driven by Lévy noise , 2014, 1407.5347.
[46] M. Hutzenthaler,et al. Numerical Approximations of Stochastic Differential Equations With Non-globally Lipschitz Continuous Coefficients , 2012, 1203.5809.
[47] J. L. Gall,et al. Brownian Motion, Martingales, and Stochastic Calculus , 2016 .
[48] Jessica Fuerst,et al. Stochastic Differential Equations And Applications , 2016 .