Relevant bibliographies by topics / Gaussian process

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Gaussian process'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Gaussian process"

1

Fearn, Tom. "Gaussian Process Regression." NIR news 24, no. 6 (2013): 23–24. http://dx.doi.org/10.1255/nirn.1392.

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Daemi, Atefeh, Hariprasad Kodamana, and Biao Huang. "Gaussian process modelling with Gaussian mixture likelihood." Journal of Process Control 81 (September 2019): 209–20. http://dx.doi.org/10.1016/j.jprocont.2019.06.007.

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O’Callaghan, Simon T., and Fabio T. Ramos. "Gaussian process occupancy maps." International Journal of Robotics Research 31, no. 1 (2012): 42–62. http://dx.doi.org/10.1177/0278364911421039.

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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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Mackay, D. J. C., and M. N. Gibbs. "Variational Gaussian process classifiers." IEEE Transactions on Neural Networks 11, no. 6 (2000): 1458–64. http://dx.doi.org/10.1109/72.883477.

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Deisenroth, Marc Peter, Carl Edward Rasmussen, and Jan Peters. "Gaussian process dynamic programming." Neurocomputing 72, no. 7-9 (2009): 1508–24. http://dx.doi.org/10.1016/j.neucom.2008.12.019.

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Chatzis, S. P., and Y. Demiris. "Echo State Gaussian Process." IEEE Transactions on Neural Networks 22, no. 9 (2011): 1435–45. http://dx.doi.org/10.1109/tnn.2011.2162109.

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Jin, Zhehao, Andong Liu, Wen-an Zhang, Li Yu, and Chenguang Yang. "Gaussian process movement primitive." Automatica 155 (September 2023): 111120. http://dx.doi.org/10.1016/j.automatica.2023.111120.

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Ou, Xiaoling, Julian Morris, and Elaine Martin. "Gaussian Process Regression for Batch Process Modelling." IFAC Proceedings Volumes 37, no. 9 (2004): 817–22. http://dx.doi.org/10.1016/s1474-6670(17)31910-9.

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Subramanian, Sandya, Riccardo Barbieri, and Emery N. Brown. "Point process temporal structure characterizes electrodermal activity." Proceedings of the National Academy of Sciences 117, no. 42 (2020): 26422–28. http://dx.doi.org/10.1073/pnas.2004403117.

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Electrodermal activity (EDA) is a direct readout of the body’s sympathetic nervous system measured as sweat-induced changes in the skin’s electrical conductance. There is growing interest in using EDA to track physiological conditions such as stress levels, sleep quality, and emotional states. Standardized EDA data analysis methods are readily available. However, none considers an established physiological feature of EDA. The sympathetically mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process. An integrate-and-fire process modeled as a Gaussian rando
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Dissertations / Theses on the topic "Gaussian process"

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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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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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Serradilla, Javier. "Gaussian process models for process monitoring and control." Thesis, University of Newcastle Upon Tyne, 2012. http://hdl.handle.net/10443/1792.

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One problem of special interest both in industry and the engineering community is that of using the enormous amounts of data routinely generated and recorded in e client process monitoring and control strategies. In statistical terms this is related to identifying those variables which exhibit unwanted or unusual process variability so that remedial action can be taken. To this end, a common approach in the literature is to reduce the problem dimensionality by using latent variable models. Customarily, the latent variables are a function of all of the original variables and monitoring is carri
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Ou, Xiaoling. "Batch process modelling with Gaussian processes." Thesis, University of Newcastle Upon Tyne, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440591.

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van, der Wilk Mark. "Sparse Gaussian process approximations and applications." Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/288347.

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Many tasks in machine learning require learning some kind of input-output relation (function), for example, recognising handwritten digits (from image to number) or learning the motion behaviour of a dynamical system like a pendulum (from positions and velocities now to future positions and velocities). We consider this problem using the Bayesian framework, where we use probability distributions to represent the state of uncertainty that a learning agent is in. In particular, we will investigate methods which use Gaussian processes to represent distributions over functions. Gaussian process mo
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Lopez, lopera Andres Felipe. "Gaussian Process Modelling under Inequality Constraints." Thesis, Lyon, 2019. https://tel.archives-ouvertes.fr/tel-02863891.

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Le conditionnement de processus gaussiens (PG) par des contraintes d’inégalité permet d’obtenir des modèles plus réalistes. Cette thèse s’intéresse au modèle de type PG proposé par maatouk (2015), obtenu par approximation finie, qui garantit que les contraintes sont satisfaites dans tout l’espace. Plusieurs contributions sont apportées. Premièrement, nous étudions l’emploi de méthodes de monte carlo par chaı̂nes de markov pour des lois multinormales tronquées. Elles fournissent un échantillonnage efficacpour des contraintes d’inégalité linéaires. Deuxièmement, nous explorons l’extension du mod
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Hanandeh, Ahmad Ali. "Nonstationary Nearest Neighbors Gaussian Process Models." University of Cincinnati / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504781089107666.

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Srinivasan, Balaji Vasan. "Gaussian process regression for model estimation." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8962.

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Thesis (M.S.) -- University of Maryland, College Park, 2008.<br>Thesis research directed by: Dept. of Electrical and Computer Engineering E. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Tran, Tien-Tam. "Constrained and Low Rank Gaussian Process on some Manifolds." Electronic Thesis or Diss., Université Clermont Auvergne (2021-...), 2023. https://theses.hal.science/tel-04529284.

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La thèse est divisée en trois parties principales, nous résumerons les principales contributions de la thèse comme suit. Processus gaussiens à faible complexité : la régression par processus gaussien s'échelonne généralement en $O(n^3)$ en termes de calcul et en $O(n^2)$ en termes d'exigences de mémoire, où $n$ représente le nombre d'observations. Cette limitation devient inapplicable pour de nombreux problèmes lorsque $n$ est grand. Dans cette thèse, nous étudions l'expansion de Karhunen-Loève des processus gaussiens, qui présente plusieurs avantages par rapport aux techniques de compression
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Melis, Alessandro. "Gaussian process emulators for 1D vascular models." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/19175/.

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One-dimensional numerical models of the arterial vasculature are capable of simulating the physics of pulse wave transmission and reflection. These models are computationally efficient and represents and ideal choice with great translational opportunities in healthcare. However, the use of these models in a patient-specific scenario is hampered by the difficulty in measuring the model inputs (parameters, boundary conditions, and initial conditions) in the clinical setting. As a result, most of the model inputs are noisy or missing, and the inputs uncertainty is transmitted to the model outputs
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Books on the topic "Gaussian process"

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Ludkovski, Michael, and Jimmy Risk. Gaussian Process Models for Quantitative Finance. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80874-6.

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Taeryon, Choi, ed. Gaussian process regression analysis for functional data. CRC Press, 2011.

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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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Cain, Michael. Prediction adjustments for asymmetric quadratic loss with a Gaussian process. University College of Wales, Dept. of Economics and Agricultural Economics, 1992.

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Neal, Radford M. Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. University of Toronto, 1997.

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Leszek, Gawarecki, ed. Stochastic analysis for Gaussian random processes and fields: With applications. CRC Press, Taylor & Francis Group, 2016.

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Østerbø, Olav. Mathematical modelling and analysis of communication networks: Transient characteristics of traffic processes and models for end-to-end delay and delay-jitter. NTNU, 2003.

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Hida, Takeyuki. Gaussian processes. American Mathematical Society, 1993.

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Hida, Takeyuki. Gaussian processes. American Mathematical Society, 1993.

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Nagoya Lévy Seminar (3rd 1990 Nagoya-shi, Japan). Gaussian random fields. Edited by Hida Takeyuki 1927-, Itō Kiyosi 1915-, International Conference on Gaussian Random Fields (1990 : Nagoya-shi, Japan), and International Congress of Mathematicians. (1990 : Kyoto, Japan). World Scientific, 1991.

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Book chapters on the topic "Gaussian process"

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Bartók-Pártay, Albert. "Gaussian Process." In The Gaussian Approximation Potential. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14067-9_3.

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Zhang, Xinhua, Novi Quadrianto, Kristian Kersting, et al. "Gaussian Process." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_324.

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Quadrianto, Novi, Kristian Kersting, and Zhao Xu. "Gaussian Process." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7502-7_108-1.

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Quadrianto, Novi, Kristian Kersting, and Zhao Xu. "Gaussian Process." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_108.

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Preumont, André. "Gaussian Process, Poisson Process." In Random Vibration and Spectral Analysis. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-017-2840-9_4.

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Ludkovski, Michael, and Jimmy Risk. "Gaussian Process Preliminaries." In SpringerBriefs in Quantitative Finance. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80874-6_1.

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Ge, Zhiqiang, and Zhihuan Song. "Non-Gaussian Process Monitoring." In Advances in Industrial Control. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4513-4_3.

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Zhang, Xinhua, Novi Quadrianto, Kristian Kersting, et al. "Gaussian Process Reinforcement Learning." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_325.

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Nelles, Oliver. "Gaussian Process Models (GPMs)." In Nonlinear System Identification. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47439-3_16.

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Nguyen, Kien, John Krumm, and Cyrus Shahabi. "Gaussian Process for Trajectories." In Spatial Gems, Volume 2. ACM, 2023. http://dx.doi.org/10.1145/3617291.3617296.

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Conference papers on the topic "Gaussian process"

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Kang, Lulu, Yuanxing Cheng, Yiwei Wang, and Chun Liu. "Energetic Variational Gaussian Process Regression." In 2024 Winter Simulation Conference (WSC). IEEE, 2024. https://doi.org/10.1109/wsc63780.2024.10838889.

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Zhou, Jiachen, Daniel Frisch, and Uwe D. Hanebeck. "Inverse Gaussian Process Interpolation for High-Quality Assumed Gaussian Filtering." In 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, 2024. http://dx.doi.org/10.1109/mfi62651.2024.10705784.

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Takegawa, Kaito, Yuya Yokoyama, and Yukihiro Hamasuna. "Gaussian Process Based Sequential Regression Models." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10651239.

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Chou, Philip A., and Ricardo L. de Queiroz. "Gaussian process transforms." In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016. http://dx.doi.org/10.1109/icip.2016.7532613.

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Hollingsworth, Peter, and Dimitri Mavris. "Gaussian Process Meta-Modeling: Comparison of Gaussian Process Training Methods." In AIAA's 3rd Annual Aviation Technology, Integration, and Operations (ATIO) Forum. American Institute of Aeronautics and Astronautics, 2003. http://dx.doi.org/10.2514/6.2003-6761.

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Neumann, Marion, Kristian Kersting, Zhao Xu, and Daniel Schulz. "Stacked Gaussian Process Learning." In 2009 Ninth IEEE International Conference on Data Mining (ICDM). IEEE, 2009. http://dx.doi.org/10.1109/icdm.2009.56.

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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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Le, Quoc V., Alex J. Smola, and Stéphane Canu. "Heteroscedastic Gaussian process regression." In the 22nd international conference. ACM Press, 2005. http://dx.doi.org/10.1145/1102351.1102413.

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Mukadam, Mustafa, Xinyan Yan, and Byron Boots. "Gaussian Process Motion planning." In 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016. http://dx.doi.org/10.1109/icra.2016.7487091.

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Huber, Marco F. "Recursive Gaussian process regression." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638281.

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Reports on the topic "Gaussian process"

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Kleedtke, Noah Andrew, Travis Justin Grove, Jesson D. Hutchinson, and Isaac James Michaud. Designing Critical Experiments using Gaussian Process Optimization. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1545754.

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Shin, Tony. Gaussian process regression for radiological contamination mapping. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1760555.

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Miller, C., B. Soper, A. Muyskens, B. Priest, M. Schneider, and D. Merl. Exploration with Scalable Gaussian Process Reinforcement Learning. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2325260.

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Graziani, Carlo. Gaussian Process Modeling For Experimental Procedure Uncertainty. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/1989793.

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Schneider, M., G. Chapline, M. Otten, and C. Miller. Gaussian Process Regression as a Riemann-Hilbert Problem. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1828667.

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Franzman, J., and C. Kamath. Understanding the Effects of Tapering on Gaussian Process Regression. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1558874.

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Filippov, A., I. Goumiri, and B. Priest. Genetic Algorithm for Hyperparameter Optimization in Gaussian Process Modeling. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1659396.

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Liu, Nian, and Matthew Sweeney. Gaussian Process Emulators for Volcanic Ash Dispersion Model Tephra2. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1879348.

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Bilionis, Ilias, and Nicholas Zabaras. Multi-output Local Gaussian Process Regression: Applications to Uncertainty Quantification. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada554929.

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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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