Characterization of non‐Gaussian, bistatic, echoes from a shipwreck.

2011 ◽  
Vol 129 (4) ◽  
pp. 2687-2687
Author(s):  
John R. Preston
Keyword(s):  
Non Gaussian

Linear dynamic errors-in-variables models

1986 ◽  
Vol 23 (A) ◽  
pp. 23-39 ◽  
Author(s):  
M. Deistler
Keyword(s):  
Dynamical Systems ◽  
Transfer Functions ◽  
Errors In Variables ◽  
Linear Dynamical Systems ◽  
Linear Dynamic ◽  
Dynamic Errors ◽  
Non Gaussian ◽  
Inputs And Outputs

Linear dynamical systems where both inputs and outputs are contaminated by errors are considered. A characterization of the sets of all observationally equivalent transfer functions is given, the role of the causality assumption is investigated and conditions for identifiability in the case of Gaussian as well as non-Gaussian observations are derived.

A probability transformation method (PTM) for the dynamic stochastic response of structures with non-Gaussian excitations

2018 ◽  
Vol 35 (5) ◽  
pp. 1978-1997 ◽  
Author(s):  
Giovanni Falsone ◽  
Rossella Laudani
Keyword(s):  
Design Methodology ◽  
Fundamental Property ◽  
Transformation Method ◽  
Computational Effort ◽  
Content Type ◽  
Probabilistic Characterization ◽  
Probability Transformation ◽  
Dynamic Stochastic ◽  
Non Gaussian

Purpose This paper aims to present an approach for the probabilistic characterization of the response of linear structural systems subjected to random time-dependent non-Gaussian actions. Design/methodology/approach Its fundamental property is working directly on the probability density functions of the actions and responses. This avoids passing through the evaluation of the response statistical moments or cumulants, reducing the computational effort in a consistent measure. Findings It is an efficient method, for both its computational effort and its accuracy, above all when the input and output processes are strongly non-Gaussian. Originality/value This approach can be considered as a dynamic generalization of the probability transformation method recently used for static applications.

Three-dimensional characterization of non-gaussian water diffusion in humans using diffusion kurtosis imaging

2006 ◽  
Vol 19 (2) ◽  
pp. 236-247 ◽  
Author(s):  
Hanzhang Lu ◽  
Jens H. Jensen ◽  
Anita Ramani ◽  
Joseph A. Helpern
Keyword(s):  
Three Dimensional ◽  
Water Diffusion ◽  
Diffusion Kurtosis Imaging ◽  
Non Gaussian ◽  
Diffusion Kurtosis

GENERATION AND CHARACTERIZATION OF ABSOLUTE EQUILIBRIUM OF COMPRESSIBLE FLOWS

2009 ◽  
Vol 19 (10) ◽  
pp. 3445-3459 ◽  
Author(s):  
GIORGIO KRSTULOVIC ◽  
CARLOS CARTES ◽  
MARC BRACHET ◽  
ENRIQUE TIRAPEGUI
Keyword(s):  
Compressible Flows ◽  
Constant Pressure ◽  
Variable Temperature ◽  
Short Review ◽  
Velocity Fields ◽  
Incompressible Limit ◽  
Pressure Variable ◽  
Temperature Relaxation ◽  
Non Gaussian

A short review is given of recent papers on the relaxation to (incompressible) absolute equilibrium. A new algorithm to construct absolute equilibrium of spectrally truncated compressible flows is described. The algorithm uses stochastic processes based on the Clebsch representation of the velocity field to generate density and velocity fields that follow by construction the absolute equilibrium stationary probability. The new method is shown to reproduce the well-known Gaussian results in the incompressible limit. The irrotational compressible absolute equilibrium case is characterized and the distribution is shown to be non-Gaussian. The high-temperature compressible spectra are found not to obey k2 scaling. Finally, oscillating behavior in constant-pressure variable-temperature relaxation is obtained, suggesting the presence of second sound.

scholarly journals Clustering Using Student t Mixture Copulas

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Till Massing
Keyword(s):  
Gaussian Mixture Models ◽  
Optimization Methods ◽  
Gaussian Mixture ◽  
Copula Models ◽  
Multimodal Distributions ◽  
Non Gaussian ◽  
Clustering Problems ◽  
Cluster Tool ◽  
Natural Way

AbstractTewari et al. (Parametric characterization of multimodal distributions with non-Gaussian modes, pp 286–292, 2011) introduced Gaussian mixture copula models (GMCM) for clustering problems which do not assume normality of the mixture components as Gaussian mixture models (GMM) do. In this paper, we propose Student t mixture copula models (SMCM) as an extension of GMCMs. GMCMs require weak assumptions, yielding a flexible fit and a powerful cluster tool. Our SMCM extension offers, in a natural way, even more flexibility than the GMCM approach. We discuss estimation issues and compare Expectation-Maximization (EM)-based with numerical simplex optimization methods. We illustrate the SMCM as a tool for image segmentation.

scholarly journals Causal Discovery with Multi-Domain LiNGAM for Latent Factors

2021 ◽  
Author(s):  
Yan Zeng ◽  
Shohei Shimizu ◽  
Ruichu Cai ◽  
Feng Xie ◽  
Michio Yamamoto ◽  
...  
Keyword(s):  
Causal Structure ◽  
Score Function ◽  
Factor Loading ◽  
Real World Data ◽  
Latent Factors ◽  
Two Phase ◽  
Loading Matrix ◽  
Non Gaussian ◽  
Causal Structures

Discovering causal structures among latent factors from observed data is a particularly challenging problem. Despite some efforts for this problem, existing methods focus on the single-domain data only. In this paper, we propose Multi-Domain Linear Non-Gaussian Acyclic Models for LAtent Factors (MD-LiNA), where the causal structure among latent factors of interest is shared for all domains, and we provide its identification results. The model enriches the causal representation for multi-domain data. We propose an integrated two-phase algorithm to estimate the model. In particular, we first locate the latent factors and estimate the factor loading matrix. Then to uncover the causal structure among shared latent factors of interest, we derive a score function based on the characterization of independence relations between external influences and the dependence relations between multi-domain latent factors and latent factors of interest. We show that the proposed method provides locally consistent estimators. Experimental results on both synthetic and real-world data demonstrate the efficacy and robustness of our approach.

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