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Relevant bibliographies by topics / Gaussian models

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Academic literature on the topic 'Gaussian models'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 June 2024

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Journal articles on the topic "Gaussian models"

1

Estrade, Anne, and Julie Fournier. "Anisotropic Gaussian wave models." Latin American Journal of Probability and Mathematical Statistics 17, no. 1 (2020): 329. http://dx.doi.org/10.30757/alea.v17-13.

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Luthi, Marcel, Thomas Gerig, Christoph Jud, and Thomas Vetter. "Gaussian Process Morphable Models." IEEE Transactions on Pattern Analysis and Machine Intelligence 40, no. 8 (2018): 1860–73. http://dx.doi.org/10.1109/tpami.2017.2739743.

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Nyman, Henrik, Johan Pensar, and Jukka Corander. "Stratified Gaussian graphical models." Communications in Statistics - Theory and Methods 46, no. 11 (2016): 5556–78. http://dx.doi.org/10.1080/03610926.2015.1105979.

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Abraham, B., and N. Balakrishna. "Inverse Gaussian Autoregressive Models." Journal of Time Series Analysis 20, no. 6 (1999): 605–18. http://dx.doi.org/10.1111/1467-9892.00161.

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Ju, Zhaojie, and Honghai Liu. "Fuzzy Gaussian Mixture Models." Pattern Recognition 45, no. 3 (2012): 1146–58. http://dx.doi.org/10.1016/j.patcog.2011.08.028.

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McNicholas, Paul David, and Thomas Brendan Murphy. "Parsimonious Gaussian mixture models." Statistics and Computing 18, no. 3 (2008): 285–96. http://dx.doi.org/10.1007/s11222-008-9056-0.

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Viroli, Cinzia, and Geoffrey J. McLachlan. "Deep Gaussian mixture models." Statistics and Computing 29, no. 1 (2017): 43–51. http://dx.doi.org/10.1007/s11222-017-9793-z.

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Stirling, A. J., and J. A. Peacock. "Non-Gaussian Isocurvature Models." Symposium - International Astronomical Union 183 (1999): 268. http://dx.doi.org/10.1017/s0074180900132851.

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Peebles (1997) has proposed a non-Gaussian χ2isocurvature model which gives rise to early galaxy assembly at redshifts ≥ 3–4. We test whether the higher order moments (skewness and kurtosis) of such a model are compatible with the moments of the observed density field, and find that in the absence of bias these models produce moments that are much higher than those measured for the APM survey (Gaztañaga, 1994). We have applied different biasing schemes to see whether bias can bring the moments down, and find that for power law, Cen-Ostriker, and censor threshold biasing schemes (Mann et al 199

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Nadarajah, Saralees. "Gaussian DCT Coefficient Models." Acta Applicandae Mathematicae 106, no. 3 (2008): 455–72. http://dx.doi.org/10.1007/s10440-008-9307-2.

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Celeux, Gilles, and Gérard Govaert. "Gaussian parsimonious clustering models." Pattern Recognition 28, no. 5 (1995): 781–93. http://dx.doi.org/10.1016/0031-3203(94)00125-6.

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Dissertations / Theses on the topic "Gaussian models"

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O'Donnell, David. "Bayesian inference for graphical Gaussian and conditional Gaussian models." Thesis, University of Southampton, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.433936.

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Drton, Mathias. "Maximum likelihood estimation in Gaussian AMP chain graph models and Gaussian ancestral graph models /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/8952.

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Sofro, A'yunin. "Convolved Gaussian process regression models for multivariate non-Gaussian data." Thesis, University of Newcastle upon Tyne, 2017. http://hdl.handle.net/10443/3723.

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Multivariate regression analysis has been developed rapidly in the last decade for dependent data. The most di cult part in multivariate cases is how to construct a crosscorrelation between response variables. We need to make sure that the covariance matrix is positive de nite which is not an easy task. Several approaches have been developed to overcome the issue. However, most of them have some limitations, such as it is hard to extend it to the case involving high dimensional variables or capture individual characteristics. It also should point out that the meaning of the cross-correlation s

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Gramacy, Robert B. "Bayesian treed Gaussian process models /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.

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Kellner, Jérémie. "Gaussian models and kernel methods." Thesis, Lille 1, 2016. http://www.theses.fr/2016LIL10177/document.

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Les méthodes à noyaux ont été beaucoup utilisées pour transformer un jeu de données initial en les envoyant dans un espace dit « à noyau » ou RKHS, pour ensuite appliquer une procédure statistique sur les données transformées. En particulier, cette approche a été envisagée dans la littérature pour tenter de rendre un modèle probabiliste donné plus juste dans l'espace à noyaux, qu'il s'agisse de mélanges de gaussiennes pour faire de la classification ou d'une simple gaussienne pour de la détection d'anomalie. Ainsi, cette thèse s'intéresse à la pertinence de tels modèles probabilistes dans ces

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Gehrmann, Helene. "Graphical Gaussian models with symmetries." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:5f69e996-3f8e-4bfa-891f-0e1ec8d0f9fb.

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This thesis is concerned with graphical Gaussian models with equality constraints on the concentration or partial correlation matrix introduced by Højsgaard and Lauritzen (2008) as RCON and RCOR models. The models can be represented by vertex and edge coloured graphs G = (V,ε), where parameters associated with equally coloured vertices or edges are restricted to being identical. In the first part of this thesis we study the problem of estimability of a non-zero model mean μ if the covariance structure Σ is restricted to satisfy the constraints of an RCON or RCOR model but is otherwise unknown.

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Kunkel, Deborah Elizabeth. "Anchored Bayesian Gaussian Mixture Models." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524134234501475.

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Schrammar, Nicolas. "On Deterministic Models for Gaussian Networks." Doctoral thesis, KTH, Kommunikationsteori, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-122275.

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In this thesis we study wireless networks modeled by the additive white Gaussian noise (AWGN) model. The AWGN capacity region of most network topologies is unknown, which means that the optimal transmission scheme is unknown as well. This motivates the search for capacity approximations and for approximately optimal schemes. Deterministic channel models have been proposed as means to approximate the AWGN model within a constant additive gap. We consider two particular models, the linear finite-field model (LFFM) and the discrete superposi- tion model (DSM). In the first part of the thesis we u

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Mohammad, Ali (Ali H. ). "Gaussian alignments in statistical translation models." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/35611.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.<br>Includes bibliographical references (leaves 52-53).<br>Machine translation software has been under development almost since the birth of the electronic computer. Current state-of-the-art methods use statistical techniques to learn how to translate from one natural language to another from a corpus of hand-translated text. The success of these techniques comes from two factors: a simple statistical model and vast training data sets. The standard agenda for improving such models i

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Malioutov, Dmitry M. 1981. "Approximate inference in Gaussian graphical models." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44906.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Includes bibliographical references (p. 161-169).<br>The focus of this thesis is approximate inference in Gaussian graphical models. A graphical model is a family of probability distributions in which the structure of interactions among the random variables is captured by a graph. Graphical models have become a powerful tool to d

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Books on the topic "Gaussian models"

1

Hrafnkelsson, Birgir, ed. Statistical Modeling Using Bayesian Latent Gaussian Models. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-39791-2.

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S, Taqqu Murad, ed. Stable non-Gaussian random processes: Stochastic models with infinite variance. Chapman & Hall, 1994.

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Ingster, Yu I., and Irina A. Suslina. Nonparametric Goodness-of-Fit Testing Under Gaussian Models. Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21580-8.

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1951-, Levendorskiĭ Serge, ed. Non-Gaussian Merton-Black-Scholes theory. World Scientific, 2002.

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Semialgebraic statistics and latent tree models. CRC Press, Taylor & Francis Group, 2016.

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I, Williams Christopher K., ed. Gaussian processes for machine learning. MIT Press, 2006.

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Rasmussen, Carl Edward. Gaussian processes for machine learning. MIT Press, 2005.

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Kocijan, Juš. Modelling and Control of Dynamic Systems Using Gaussian Process Models. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-21021-6.

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Das, Sanjiv R. Poisson-Gaussian processes and the bond markets. National Bureau of Economic Research, 1998.

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Ser-Huang, Poon, and Rockinger Michael, eds. Financial Modeling Under Non-Gaussian Distributions. Springer London, 2007.

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Book chapters on the topic "Gaussian models"

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Zannetti, Paolo. "Gaussian Models." In Air Pollution Modeling. Springer US, 1990. http://dx.doi.org/10.1007/978-1-4757-4465-1_7.

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Yu, Dong, and Li Deng. "Gaussian Mixture Models." In Automatic Speech Recognition. Springer London, 2014. http://dx.doi.org/10.1007/978-1-4471-5779-3_2.

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Højsgaard, Søren, David Edwards, and Steffen Lauritzen. "Gaussian Graphical Models." In Graphical Models with R. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-2299-0_4.

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Reynolds, Douglas. "Gaussian Mixture Models." In Encyclopedia of Biometrics. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_196.

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Reynolds, Douglas. "Gaussian Mixture Models." In Encyclopedia of Biometrics. Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7488-4_196.

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Liang, Faming, and Bochao Jia. "Gaussian Graphical Models." In Sparse Graphical Modeling for High Dimensional Data. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9780429061189-2.

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Grabe, Michael. "Models and Approaches." In Generalized Gaussian Error Calculus. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03305-6_2.

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Doukhan, Paul. "Gaussian Chaos." In Stochastic Models for Time Series. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-76938-7_5.

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Hristopulos, Dionissios T. "Beyond the Gaussian Models." In Advances in Geographic Information Science. Springer Netherlands, 2020. http://dx.doi.org/10.1007/978-94-024-1918-4_14.

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Liu, Honghai, Zhaojie Ju, Xiaofei Ji, Chee Seng Chan, and Mehdi Khoury. "Fuzzy Gaussian Mixture Models." In Human Motion Sensing and Recognition. Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-53692-6_5.

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Conference papers on the topic "Gaussian models"

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Chan, Antoni B., and Daxiang Dong. "Generalized Gaussian process models." In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995688.

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Maas, Ryan, Jeremy Hyrkas, Olivia Grace Telford, Magdalena Balazinska, Andrew Connolly, and Bill Howe. "Gaussian Mixture Models Use-Case." In the 3rd VLDB Workshop. ACM Press, 2015. http://dx.doi.org/10.1145/2803140.2803143.

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Sabeti, Elyas, and Anders Host-Madsen. "Atypicality for vector Gaussian models." In 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2015. http://dx.doi.org/10.1109/globalsip.2015.7418211.

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Schmerr, Lester W., Ana Lopez-Sanchez, and Alexander Sedov. "Generating the Gaussian Basis Functions for Multi-Gaussian Beam Models." In REVIEW OF PROGRESS IN QUANTITATIVE NONDESTRUCTIVE EVALUATION. AIP, 2007. http://dx.doi.org/10.1063/1.2718061.

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Kozierski, Piotr, Talar Sadalla, and Dariusz Horla. "Non-Gaussian models in particle filters." In 2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR ). IEEE, 2015. http://dx.doi.org/10.1109/mmar.2015.7283858.

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Li, Yajun. "Mathematical models for diode laser beams." In OSA Annual Meeting. Optica Publishing Group, 1991. http://dx.doi.org/10.1364/oam.1991.thr5.

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It is known that there are two mathematical models for the elliptic beams generated by semi conductor laser diodes. The first model is the simple Gaussian model,1 in which two Gaussian distributions with different widths are employed to describe the light distribution over the elliptic cross-section of the beam. The second model is known as the Lorentzian-Gaussian model2 which was established in a study of the fact that the Gaussian distribution is valid only for the light field parallel to the junction and in the perpendicular direction the field is described by the Loretzian distribution. In

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Nurminen, Henri, Robert Piche, and Simon Godsill. "Gaussian flow sigma point filter for nonlinear Gaussian state-space models." In 2017 20th International Conference on Information Fusion (Fusion). IEEE, 2017. http://dx.doi.org/10.23919/icif.2017.8009682.

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Petelin, Dejan, Jan Sindelar, Jan Prikryl, and Jus Kocijan. "Financial modeling using Gaussian process models." In 2011 IEEE 6th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2011. http://dx.doi.org/10.1109/idaacs.2011.6072854.

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Nabors, Keith, Tze-Ting Fang, Hung-Wen Chang, and Kenneth S. Kundert. "Lumped interconnect models via Gaussian quadrature." In the 34th annual conference. ACM Press, 1997. http://dx.doi.org/10.1145/266021.266032.

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Speekenbrink, Maarten. "Identifiability of Gaussian Bayesian bandit models." In 2019 Conference on Cognitive Computational Neuroscience. Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1335-0.

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Reports on the topic "Gaussian models"

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Leger, Flavien, Guoshen Yu, and Guillermo Sapiro. Efficient Matrix Completion with Gaussian Models. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada540730.

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Yu, Guoshen, and Guillermo Sapiro. Statistical Compressive Sensing of Gaussian Mixture Models. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada540728.

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Chen, Stanley F., and Ronald Rosenfeld. A Gaussian Prior for Smoothing Maximum Entropy Models. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada360974.

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Chernozhukov, Victor, Martin Spindler, Jannis Kück, and Sven Klaassen. Uniform inference in high-dimensional Gaussian graphical models. The IFS, 2019. http://dx.doi.org/10.1920/wp.cem.2019.2919.

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Hamilton, James, and Jing Cynthia Wu. Identification and Estimation of Gaussian Affine Term Structure Models. National Bureau of Economic Research, 2012. http://dx.doi.org/10.3386/w17772.

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Krommes, J. A. Non-Gaussian statistics, classical field theory, and realizable Langevin models. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/211662.

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Arndt, Channing. An Introduction to Systematic Sensitivity Analysis via Gaussian Quadrature. GTAP Technical Paper, 2000. http://dx.doi.org/10.21642/gtap.tp02.

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Economists recognize that results from simulation models are dependent, sometimes highly dependent, on values employed for critical exogenous variables. To account for this, analysts sometimes conduct sensitivity analysis with respect to key exogenous variables. This paper presents a practical approach for conducting systematic sensitivity analysis, called Gaussian quadrature. The approach views key exogenous variables as random variables with associated distributions. It produces estimates of means and standard deviations of model results while requiring a limited number of solves of the mode

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Piper, A. A field study evaluation of short-term refined Gaussian dispersion models. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/671861.

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Weiss, Yair, and William T. Freeman. Correctness of Belief Propogation in Gaussian Graphical Models of Arbitrary Topology. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada603879.

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Swiler, Laura, Mamikon Gulian, Ari Frankel, John Jakeman, and Cosmin Safta. LDRD Project Summary: Incorporating physical constraints into Gaussian process surrogate models. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1668928.

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