Systematic physics constrained parameter estimation of stochastic differential equations
作者： Daniel Peavoy , Christian L.E. Franzke , Gareth O. Roberts
DOI: 10.1016/J.CSDA.2014.10.011
关键词:
摘要: A systematic Bayesian framework is developed for physics constrained parameter inference of stochastic differential equations (SDE) from partial observations. Physical constraints are derived climate models but applicable many fluid systems. condition global stability based on energy conservation. Stochastic globally stable when a quadratic form, which related to the cubic nonlinear operator, negative definite. new algorithm efficient sampling such definite matrices and also imputing unobserved data improve accuracy estimates. The performance this evaluated two conceptual models.
harvard.edu
core.ac.uk
mpg.de
warwick.ac.uk
elsevier.com
sci-hub.se 参考文章(39)
Alexandros Beskos, Omiros Papaspiliopoulos, Gareth O. Roberts, A Factorisation of Diffusion Measure and Finite Sample Path Constructions Methodology and Computing in Applied Probability. ,vol. 10, pp. 85- 104 ,(2008) , 10.1007/S11009-007-9060-4
Christian Franzke, Predictability of extreme events in a nonlinear stochastic-dynamical model Physical Review E. ,vol. 85, pp. 031134- ,(2012) , 10.1103/PHYSREVE.85.031134
Gareth O Roberts, Osnat Stramer, On inference for partially observed nonlinear diffusion models using the Metropolis–Hastings algorithm Biometrika. ,vol. 88, pp. 603- 621 ,(2001) , 10.1093/BIOMET/88.3.603
Yacine Ait-Sahalia, Maximum Likelihood Estimation of Discretely Sampled Diffusions: A Closed‐form Approximation Approach Econometrica. ,vol. 70, pp. 223- 262 ,(2002) , 10.1111/1468-0262.00274
A. J. Majda, C. Franzke, D. Crommelin, Normal forms for reduced stochastic climate models Proceedings of the National Academy of Sciences of the United States of America. ,vol. 106, pp. 3649- 3653 ,(2009) , 10.1073/PNAS.0900173106
Andrew J Majda, Christian Franzke, Boualem Khouider, An applied mathematics perspective on stochastic modelling for climate Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. ,vol. 366, pp. 2427- 2453 ,(2008) , 10.1098/RSTA.2008.0012
A. Golightly, D.J. Wilkinson, Bayesian inference for nonlinear multivariate diffusion models observed with error Computational Statistics & Data Analysis. ,vol. 52, pp. 1674- 1693 ,(2008) , 10.1016/J.CSDA.2007.05.019
Andrew J. Majda, Ilya Timofeyev, Eric Vanden Eijnden, A mathematical framework for stochastic climate models Communications on Pure and Applied Mathematics. ,vol. 54, pp. 891- 974 ,(2001) , 10.1002/CPA.1014
S Siegert, R Friedrich, J Peinke, Analysis of data sets of stochastic systems Physics Letters A. ,vol. 243, pp. 275- 280 ,(1998) , 10.1016/S0375-9601(98)00283-7
A. R. Pedersen, A new approach to maximum likelihood estimation for stochastic differential equations based on discrete observations Scandinavian Journal of Statistics. ,vol. 22, pp. 55- 71 ,(1995)