Estimation of drift and diffusion functions from time series data: a maximum likelihood framework.
about
A fixed mass method for the Kramers-Moyal expansion--application to time series with outliers.Extended Kramers-Moyal analysis applied to optical trapping.Markov chain Monte Carlo approach to parameter estimation in the FitzHugh-Nagumo model.Uncovering wind turbine properties through two-dimensional stochastic modeling of wind dynamics.Only through perturbation can relaxation times be estimated.Probing small-scale intermittency with a fluctuation theorem.
P2860
Estimation of drift and diffusion functions from time series data: a maximum likelihood framework.
description
2012 nî lūn-bûn
@nan
2012 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
name
Estimation of drift and diffus ...... maximum likelihood framework.
@ast
Estimation of drift and diffus ...... maximum likelihood framework.
@en
Estimation of drift and diffus ...... maximum likelihood framework.
@nl
type
label
Estimation of drift and diffus ...... maximum likelihood framework.
@ast
Estimation of drift and diffus ...... maximum likelihood framework.
@en
Estimation of drift and diffus ...... maximum likelihood framework.
@nl
prefLabel
Estimation of drift and diffus ...... maximum likelihood framework.
@ast
Estimation of drift and diffus ...... maximum likelihood framework.
@en
Estimation of drift and diffus ...... maximum likelihood framework.
@nl
P2860
P1433
P1476
Estimation of drift and diffus ...... maximum likelihood framework.
@en
P2093
David Kleinhans
P2860
P304
P356
10.1103/PHYSREVE.85.026705
P407
P433
P577
2012-02-16T00:00:00Z