Markov Chain Monte Carlo and Numerical Differential Equations
作者： J. M. Sanz-Serna
DOI: 10.1007/978-3-319-01300-8_2
关键词:
摘要: The aim of this contribution is to provide a readable account Markov Chain Monte Carlo methods, with particular emphasis on their relations the numerical integration deterministic and stochastic differential equations. exposition largely based experiments avoids mathematical technicalities. presentation self-contained includes tutorial sections processes, chains, equations Hamiltonian dynamics. Metropolis Random-Walk algorithm, adjusted Langevin algorithm Hybrid are discussed in detail, including some recent results.
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sanzserna.org 参考文章(43)
Peter J. Huber, Introduction to Metropolis, Rosenbluth, Rosenbluth, Teller, and Teller (1953) Equations of State Calculations by Fast Computing Machines. J. Chem. Phys .,21, 1087–1092. and Geman and Geman (1984) Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Trans. Pattern Anal. Machine Intelligence ,6, 721–741. Springer, New York, NY. pp. 123- 139 ,(1997) , 10.1007/978-1-4612-0667-5_6
Robert B. Leighton, Feynman Lectures on Physics ,(1971)
Radford M. Neal, Probabilistic Inference Using Markov Chain Monte Carlo Methods ,(2011)
Tamar Schlick, Molecular modeling and simulation ,(2002)
Tony Lelièvre, Mathias Rousset, Gabriel Stoltz, Free Energy Computations: A Mathematical Perspective ,(2010)
政清 宮沢, P. Bremaud 著, Markov Chains, (Gibbs fields, Monte Carlo simulation and Queues), Springer-Verlag, 1999年 オペレーションズ・リサーチ : 経営の科学. ,vol. 45, pp. 85- 86 ,(2000)
Avner Friedman, Stochastic Differential Equations and Applications ,(1975)
Xuerong Mao, Stochastic Differential Equations and Applications ,(2008)
Monte Carlo Simulation John Wiley & Sons, Ltd. pp. 133- 160 ,(2013) , 10.1002/9781119973515.CH9
Sebastian Reich, Benedict J Leimkuhler, Simulating Hamiltonian Dynamics ,(2005)