Proceedings of the 2010 American Control Conference 2010
DOI: 10.1109/acc.2010.5531345
|View full text |Cite
|
Sign up to set email alerts
|

A maximum likelihood approach to recursive polynomial chaos parameter estimation

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

0
13
0

Year Published

2012
2012
2017
2017

Publication Types

Select...
4
1
1

Relationship

0
6

Authors

Journals

citations
Cited by 17 publications
(13 citation statements)
references
References 9 publications
0
13
0
Order By: Relevance
“…Hence this can be used wherever samples are needed, and this device has actually been employed, i.e. in the MCMC method [22,16,32,17] as described in Subsection 3.1, or in the EnKF method [18] described in Subsection 3.2.…”
Section: Polynomial Chaos Projectionmentioning
confidence: 99%
See 2 more Smart Citations
“…Hence this can be used wherever samples are needed, and this device has actually been employed, i.e. in the MCMC method [22,16,32,17] as described in Subsection 3.1, or in the EnKF method [18] described in Subsection 3.2.…”
Section: Polynomial Chaos Projectionmentioning
confidence: 99%
“…This can be then sampled by letting the Markov chain run for a sufficiently long time, although the samples are not independent in this case. With the intention of accelerating the MCMC method some authors [22,16,32,17] have introduced stochastic spectral methods into the computation. Expanding the prior random process into a polynomial chaos (PCE) or a Karhunen-Loève expansion (KLE) (e.g.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…We can assume the vector field models as a nonlinear system, where we only have access to the output measurements defined as the trajectory positions. Previous works related with the identification of nonlinear systems already encompass a variety of approaches [8], [9], [10], [11], [12], [13]. The algorithms used to estimate parameters include expectation-maximization [14] and particle filters [15], [16].…”
Section: Introductionmentioning
confidence: 99%
“…Some of these techniques are: simulated maximum likelihood methods [such as Markov Chain Monte Carlo (MCMC) techniques], [11][12][13][14][15][16] expansion of the likelihood function using Hermite polynomial basis functions, [17,18] solving the FokkerPlanck equation numerically [19][20][21] and recursive maximum likelihood parameter estimation using polynomial chaos theory. [22] Benefits and drawbacks of these techniques are summarised by Lindstrom. [9] An approximate ML method was proposed by Kristensen et al [23] In Kristensen's method, a Gaussian distribution is assumed for the likelihood function and the mean and variance of the likelihood function are estimated using an extended Kalman filter (EKF).…”
mentioning
confidence: 99%