Sampling conditioned diffusions
作者： Martin Hairer , Andrew Stuart , Jochen Voß
DOI: 10.1017/CBO9781139107020.009
关键词: Statistics 、 Generalization 、 Signal 、 Distribution (mathematics) 、 Smoothing 、 Mathematics 、 Sampling (statistics) 、 Sample (statistics) 、 Applied mathematics
摘要: For many practical problems it is useful to be able sample conditioned diffusions on a computer (e.g. in filtering/ smoothing from the distribution of unknown signal given known observations). We present recently developed, SPDE-based method tackle this problem. The an infinite-dimensional generalization Langevin sampling technique.
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warwick.ac.uk参考文章(7)
Steven E. Shreve, Ioannis Karatzas, Brownian Motion and Stochastic Calculus ,(1987)
M. Hairer, A. M. Stuart, J. Voss, P. Wiberg, Analysis of SPDEs arising in path sampling. Part I: The Gaussian case Communications in Mathematical Sciences. ,vol. 3, pp. 587- 603 ,(2005) , 10.4310/CMS.2005.V3.N4.A8
Martin Hairer, A. M. Stuart, J. Voss, ANALYSIS OF SPDES ARISING IN PATH SAMPLING PART II: THE NONLINEAR CASE Annals of Applied Probability. ,vol. 17, pp. 1657- 1706 ,(2007) , 10.1214/07-AAP441
A. Apte, C. K. R. T. Jones, A. M. Stuart, J. Voss, Data assimilation: Mathematical and statistical perspectives International Journal for Numerical Methods in Fluids. ,vol. 56, pp. 1033- 1046 ,(2008) , 10.1002/FLD.1698
Neil Shephard, Siddhartha Chib, Michael K Pitt, Likelihood based inference for diffusion driven models. Research Papers in Economics. ,(2004)
A. Apte, M. Hairer, A.M. Stuart, J. Voss, Sampling the posterior: An approach to non-Gaussian data assimilation Physica D: Nonlinear Phenomena. ,vol. 230, pp. 50- 64 ,(2007) , 10.1016/J.PHYSD.2006.06.009
ALEXANDROS BESKOS, GARETH ROBERTS, ANDREW STUART, JOCHEN VOSS, MCMC methods for diffusion bridges Stochastics and Dynamics. ,vol. 08, pp. 319- 350 ,(2008) , 10.1142/S0219493708002378