Estimating the probability density function of nonlinearstochastic processes by use of asymptotic expansions in theKubo number

Ravaud, Mathieu Mure
Kavvas, M. Levent
Ercan, Ali


Estimating parameters of a multiple autoregressive model by the modified maximum likelihood method
TÜRKER BAYRAK, ÖZLEM; Akkaya, Ayşen (Elsevier BV, 2010-02-15)
We consider a multiple autoregressive model with non-normal error distributions, the latter being more prevalent in practice than the usually assumed normal distribution. Since the maximum likelihood equations have convergence problems (Puthenpura and Sinha, 1986) [11], we work Out modified maximum likelihood equations by expressing the maximum likelihood equations in terms of ordered residuals and linearizing intractable nonlinear functions (Tiku and Suresh, 1992) [8]. The solutions, called modified maximu...
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© 2021 IEEE.Graph models provide flexible tools for the representation and analysis of signals defined over irregular domains such as social or sensor networks. However, in real applications data observations are often not available over the whole graph, due to practical problems such as sensor failure or connection loss. In this paper, we study the estimation of partially observed graph signals on multiple graphs. We learn a sparse representation of partially observed graph signals over spectrally concentr...
Estimating Flow Patterns and Frictional Pressure Losses of Two-Phase Fluids in Horizontal Wellbores Using Artificial Neural Networks
Ozbayoglu, E. M.; Ozbayoglu, M. A. (Informa UK Limited, 2009-01-01)
Underbalanced drilling achieved by gasified fluids is a very commonly used technique in many petroleum-engineering applications. This study estimates the flow patterns and frictional pressure losses of two-phase fluids flowing through horizontal annular geometries using artificial neural networks rather than using conventional mechanistic models. Experimental data is collected from experiments conducted at METU-PETE Flow Loop as well as data from literature in order to train the artificial neural networks. ...
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Estimating Network Kinetics of the MAPK/ERK Pathway Using Biochemical Data
Purutçuoğlu Gazi, Vilda (Hindawi Limited, 2012)
The MAPK/ERK pathway is a major signal transduction system which regulates many fundamental cellular processes including the growth control and the cell death. As a result of these roles, it has a crucial importance in cancer as well as normal developmental processes. Therefore, it has been intensively studied resulting in a wealth of knowledge about its activation. It is also well documented that the activation kinetics of the pathway is crucial to determine the nature of the biological response. However, ...
Citation Formats
M. M. Ravaud, M. L. Kavvas, and A. Ercan, “Estimating the probability density function of nonlinearstochastic processes by use of asymptotic expansions in theKubo number,” NONLINEAR STUDIES, vol. 27, no. 1, pp. 1–22, 2020, Accessed: 00, 2022. [Online]. Available: