Academic literature on the topic 'NonGaussianità'
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Journal articles on the topic "NonGaussianità"
Lehners, Jean-Luc. "Ekpyrotic Nongaussianity: A Review." Advances in Astronomy 2010 (2010): 1–19. http://dx.doi.org/10.1155/2010/903907.
Full textBramante, Joseph. "Generically large nongaussianity in small multifield inflation." Journal of Cosmology and Astroparticle Physics 2015, no. 07 (July 7, 2015): 006. http://dx.doi.org/10.1088/1475-7516/2015/07/006.
Full textBarnaby, Neil, Ryo Namba, and Marco Peloso. "Phenomenology of a pseudo-scalar inflaton: naturally large nongaussianity." Journal of Cosmology and Astroparticle Physics 2011, no. 04 (April 7, 2011): 009. http://dx.doi.org/10.1088/1475-7516/2011/04/009.
Full textHuang, Yufen, Ching-Ren Cheng, and Tai-Ho Wang. "Pair-perturbation influence functions of nongaussianity by projection pursuit." Computational Statistics & Data Analysis 52, no. 8 (April 2008): 3971–87. http://dx.doi.org/10.1016/j.csda.2008.01.009.
Full textYang, Z., A. T. Walden, and E. J. McCoy. "Correntropy: Implications of nonGaussianity for the moment expansion and deconvolution." Signal Processing 91, no. 4 (April 2011): 864–76. http://dx.doi.org/10.1016/j.sigpro.2010.09.004.
Full textYin, Hujun, and Israr Hussain. "Independent component analysis and nongaussianity for blind image deconvolution and deblurring." Integrated Computer-Aided Engineering 15, no. 3 (May 12, 2008): 219–28. http://dx.doi.org/10.3233/ica-2008-15302.
Full textALLEGRA, MICHELE, PAOLO GIORDA, and MATTEO G. A. PARIS. "DECOHERENCE OF GAUSSIAN AND NONGAUSSIAN PHOTON-NUMBER ENTANGLED STATES IN A NOISY CHANNEL." International Journal of Quantum Information 09, supp01 (January 2011): 27–38. http://dx.doi.org/10.1142/s0219749911007009.
Full textHyvärinen, Aapo. "Complexity Pursuit: Separating Interesting Components from Time Series." Neural Computation 13, no. 4 (April 1, 2001): 883–98. http://dx.doi.org/10.1162/089976601300014394.
Full textAbramo, L. Raul, and Thiago S. Pereira. "Testing Gaussianity, Homogeneity, and Isotropy with the Cosmic Microwave Background." Advances in Astronomy 2010 (2010): 1–25. http://dx.doi.org/10.1155/2010/378203.
Full textHurri, Jarmo, and Aapo Hyvärinen. "Simple-Cell-Like Receptive Fields Maximize Temporal Coherence in Natural Video." Neural Computation 15, no. 3 (March 1, 2003): 663–91. http://dx.doi.org/10.1162/089976603321192121.
Full textDissertations / Theses on the topic "NonGaussianità"
Albarelli, Francesco. "Nonlinearity as a resource for nonclassicality." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amslaurea.unibo.it/8300/.
Full textGalliano, Dominic. "Searching for isocurvature non-Gaussianity in the CMB trispectrum." Thesis, University of Portsmouth, 2014. https://researchportal.port.ac.uk/portal/en/theses/searching-for-isocurvature-nongaussianity-in-the-cmb-trispectrum(22a58c55-fd97-46eb-bd65-2d7421460f9e).html.
Full textTellarini, Matteo. "Primordial non-Gaussianity in the large-scale structure of the Universe." Thesis, University of Portsmouth, 2016. https://researchportal.port.ac.uk/portal/en/theses/primordial-nongaussianity-in-the-largescale-structure-of-the-universe(dd11bbd3-33e9-471f-8713-1efd4b6a6dbb).html.
Full textKalus, Benedict Konrad Wilhelm. "The distribution of galaxies as a test of primordial non-Gaussianity." Thesis, University of Portsmouth, 2018. https://researchportal.port.ac.uk/portal/en/theses/the-distribution-of-galaxies-as-a-test-of-primordial-nongaussianity(e07ef8fd-9509-4992-91fc-9c69465effec).html.
Full textCheng, Chin-Zen, and 鄭清仁. "Influence Analysis of Nongaussianity by Applying Projection Pursuit." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/53766687957568781608.
Full text國立中正大學
統計科學所
94
Gaussian distribution is the least structured from the information-theoretic point of view. In this thesis, the projection pursuit is performed by finding the most nongaussian projection to explore the clustering structure of the data. We use kurtosis as a measure of nongaussianity to find the projection direction. Kurtosis is well known to be sensitive to abnormal observations, henceforth the projection direction will be essentially affected by unusual points. The perturbation theory provides a useful tool in sensitivity analysis. In this thesis, we develop influence functions for the projection direction to investigate the influence of unusual observations. It is well-known that single-perturbation diagnostics can suffer from the masking effect. Hence we also develop the pair-perturbation influence functions to detect the masked influential points and outliers. A simulated data and a specific data example are provided to illustrate the applications of these approaches.
Book chapters on the topic "NonGaussianità"
Wang, Gang, Xin Xu, and Dewen Hu. "Local Stability Analysis of Maximum Nongaussianity Estimation in Independent Component Analysis." In Advances in Neural Networks - ISNN 2006, 1133–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11759966_167.
Full textConference papers on the topic "NonGaussianità"
Liu, Keying, and Rui Li. "Source Extraction Using Novel NonGaussianity Measure." In 2010 International Conference on Intelligent Computing and Cognitive Informatics (ICICCI). IEEE, 2010. http://dx.doi.org/10.1109/icicci.2010.78.
Full textZhao Liquan and Jia Yanfei. "Harmonic and interharmonic estimation using improved complex maximization of nongaussianity algorithm." In 2010 2nd International Conference on Computer Engineering and Technology. IEEE, 2010. http://dx.doi.org/10.1109/iccet.2010.5486310.
Full textNovey, M., and T. Adali. "Adaptable Nonlinearity for Complex Maximization of Nongaussianity and a Fixed-Point Algorithm." In 2006 IEEE Signal Processing Society Workshop. IEEE, 2006. http://dx.doi.org/10.1109/mlsp.2006.275526.
Full textGorecka, Joanna. "Noncerebral waves detection from frontal brain electrical activity using the quantitative measure of nongaussianity." In 2014 19th International Conference on Methods & Models in Automation & Robotics (MMAR). IEEE, 2014. http://dx.doi.org/10.1109/mmar.2014.6957446.
Full textChen, Mo, Temujin Gautama, Dragan Obradovic, Jonathon Chambers, and Danilo Mandic. "Exploiting Signal Nongaussianity and Nonlinearity for Performance Assessment of Adaptive Filtering Algorithms: Qualitative Performance of Kalman Filter." In 2006 IEEE Nonlinear Statistical Signal Processing Workshop. IEEE, 2006. http://dx.doi.org/10.1109/nsspw.2006.4378837.
Full textGhavami, Siavash, and Bahman Abolhassani. "Detection of DS-SS Signals over Fading Channels without Prior Knowledge of Spreading Sequence by Measuring Signal Nongaussianity." In 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications. IEEE, 2006. http://dx.doi.org/10.1109/pimrc.2006.254353.
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