Variational estimation of the drift for stochastic differential equations from the empirical density
| Autor: | Manfred Opper, Philipp Batz, Andreas Ruttor |
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| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Nonparametric statistics FOS: Physical sciences Statistical and Nonlinear Physics Probability and statistics Function (mathematics) 01 natural sciences Regularization (mathematics) Noise (electronics) 010305 fluids & plasmas Stochastic differential equation Kernel (statistics) Physics - Data Analysis Statistics and Probability 0103 physical sciences Applied mathematics Minification Statistics Probability and Uncertainty 010306 general physics Data Analysis Statistics and Probability (physics.data-an) Mathematics |
| Popis: | We present a method for the nonparametric estimation of the drift function of certain types of stochastic differential equations from the empirical density. It is based on a variational formulation of the Fokker-Planck equation. The minimization of an empirical estimate of the variational functional using kernel based regularization can be performed in closed form. We demonstrate the performance of the method on second order, Langevin-type equations and show how the method can be generalized to other noise models. 12 pages, 5 figures |
| Databáze: | OpenAIRE |
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