The Coloured Noise Expansion and Parameter Estimation of Diffusion Processes
暂无分享,去创建一个
[1] Frank E. Grubbs,et al. An Introduction to Probability Theory and Its Applications , 1951 .
[2] Hermann Singer. Parameter Estimation of Nonlinear Stochastic Differential Equations: Simulated Maximum Likelihood versus Extended Kalman Filter and Itô-Taylor Expansion , 2002 .
[3] Darren J. Wilkinson,et al. Bayesian inference for nonlinear multivariate diffusion models observed with error , 2008, Comput. Stat. Data Anal..
[4] A. Doucet,et al. Particle filters for stochastic differential equations of nonlinear diffusions , 2005 .
[5] Dan Cornford,et al. A Comparison of Variational and Markov Chain Monte Carlo Methods for Inference in Partially Observed Stochastic Dynamic Systems , 2007, J. Signal Process. Syst..
[6] B. Øksendal. Stochastic Differential Equations , 1985 .
[7] Karl J. Friston,et al. Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models , 2009, Physica D. Nonlinear phenomena.
[8] A. Jazwinski. Stochastic Processes and Filtering Theory , 1970 .
[9] D. Gillespie. The chemical Langevin equation , 2000 .
[10] S. Roweis,et al. Continuous Time Particle Filtering for fMRI , 2007 .
[11] L. Frankcombe,et al. A stochastic dynamical systems view of the Atlantic Multidecadal Oscillation , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[12] J. Shawe-Taylor,et al. Approximate inference in continuous time Gaussian-Jump processes , 2010 .
[13] D. Sherrington. Stochastic Processes in Physics and Chemistry , 1983 .
[14] Michael I. Jordan,et al. Learning with Mixtures of Trees , 2001, J. Mach. Learn. Res..
[15] P. Bickel,et al. Obstacles to High-Dimensional Particle Filtering , 2008 .
[16] Feller William,et al. An Introduction To Probability Theory And Its Applications , 1950 .
[17] Wuan Luo. Wiener Chaos Expansion and Numerical Solutions of Stochastic Partial Differential Equations , 2006 .
[18] R. Wolpert,et al. Weak convergence of stochastic neuronal models , 1985 .
[19] A. S. Formulation. Particle Smoothing in Continuous Time: A Fast Approach via Density Estimation , 2011 .
[20] K. Vahala. Handbook of stochastic methods for physics, chemistry and the natural sciences , 1986, IEEE Journal of Quantum Electronics.
[21] P. Kloeden,et al. Numerical Solution of Stochastic Differential Equations , 1992 .
[22] Dan Cornford,et al. Variational Inference for Diffusion Processes , 2007, NIPS.
[23] P. Fearnhead,et al. Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion) , 2006 .
[24] G. Roberts,et al. Retrospective exact simulation of diffusion sample paths with applications , 2006 .
[25] Yacine Aït-Sahalia. Maximum Likelihood Estimation of Discretely Sampled Diffusions: A Closed‐form Approximation Approach , 2002 .
[26] S. Särkkä,et al. On Unscented Kalman Filtering for State Estimation of Continuous-Time Nonlinear Systems , 2007, IEEE Transactions on Automatic Control.
[27] Dan Cornford,et al. Gaussian Process Approximations of Stochastic Differential Equations , 2007, Gaussian Processes in Practice.
[28] G. Roberts,et al. Monte Carlo Maximum Likelihood Estimation for Discretely Observed Diffusion Processes , 2009, 0903.0290.
[29] S. Hosseini,et al. A new adaptive Runge-Kutta method for stochastic differential equations , 2007 .
[30] A. Gallant,et al. Numerical Techniques for Maximum Likelihood Estimation of Continuous-Time Diffusion Processes , 2002 .
[31] Ioannis Karatzas,et al. Brownian Motion and Stochastic Calculus , 1987 .
[32] Yacine Aït-Sahalia. Closed-Form Likelihood Expansions for Multivariate Diffusions , 2008 .
[33] Hermann Singer. Nonlinear continuous time modeling approaches in panel research , 2008 .
[34] S. Shreve,et al. Stochastic differential equations , 1955, Mathematical Proceedings of the Cambridge Philosophical Society.
[35] R. C. Merton,et al. Theory of Rational Option Pricing , 2015, World Scientific Reference on Contingent Claims Analysis in Corporate Finance.
[36] M. Pitt,et al. Likelihood based inference for diffusion driven models , 2004 .
[37] A. Doucet,et al. Particle Markov chain Monte Carlo methods , 2010 .