Journal articles on the topic 'Gaussian Regression Processes'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Gaussian Regression Processes.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
Boloix-Tortosa, Rafael, Juan Jose Murillo-Fuentes, Francisco Javier Payan-Somet, and Fernando Perez-Cruz. "Complex Gaussian Processes for Regression." IEEE Transactions on Neural Networks and Learning Systems 29, no. 11 (November 2018): 5499–511. http://dx.doi.org/10.1109/tnnls.2018.2805019.
Full textMunoz-Gonzalez, Luis, Miguel Lazaro-Gredilla, and Anibal R. Figueiras-Vidal. "Divisive Gaussian Processes for Nonstationary Regression." IEEE Transactions on Neural Networks and Learning Systems 25, no. 11 (November 2014): 1991–2003. http://dx.doi.org/10.1109/tnnls.2014.2301951.
Full textTerry, Nick, and Youngjun Choe. "Splitting Gaussian processes for computationally-efficient regression." PLOS ONE 16, no. 8 (August 24, 2021): e0256470. http://dx.doi.org/10.1371/journal.pone.0256470.
Full textWu, Xing Hui, and Yu Ping Zhou. "Regression and Classification Method Based on Gaussian Processes." Advanced Materials Research 971-973 (June 2014): 1949–52. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1949.
Full textGonçalves, Ítalo Gomes, Felipe Guadagnin, and Diogo Peixoto Cordova. "Learning spatial patterns with variational Gaussian processes: Regression." Computers & Geosciences 161 (April 2022): 105056. http://dx.doi.org/10.1016/j.cageo.2022.105056.
Full textPerez-Cruz, F., J. J. Murillo-Fuentes, and S. Caro. "Nonlinear Channel Equalization With Gaussian Processes for Regression." IEEE Transactions on Signal Processing 56, no. 10 (October 2008): 5283–86. http://dx.doi.org/10.1109/tsp.2008.928512.
Full textZhang, Tong. "Approximation Bounds for Some Sparse Kernel Regression Algorithms." Neural Computation 14, no. 12 (December 1, 2002): 3013–42. http://dx.doi.org/10.1162/089976602760805395.
Full textCarvalho, Ruan M., Iago G. L. Rosa, Diego E. B. Gomes, Priscila V. Z. C. Goliatt, and Leonardo Goliatt. "Gaussian processes regression for cyclodextrin host-guest binding prediction." Journal of Inclusion Phenomena and Macrocyclic Chemistry 101, no. 1-2 (July 12, 2021): 149–59. http://dx.doi.org/10.1007/s10847-021-01092-4.
Full textMunoz-Gonzalez, Luis, Miguel Lazaro-Gredilla, and Anibal R. Figueiras-Vidal. "Laplace Approximation for Divisive Gaussian Processes for Nonstationary Regression." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 3 (March 1, 2016): 618–24. http://dx.doi.org/10.1109/tpami.2015.2452914.
Full textLeithead, W. E., Kian Seng Neo, and D. J. Leith. "GAUSSIAN REGRESSION BASED ON MODELS WITH TWO STOCHASTIC PROCESSES." IFAC Proceedings Volumes 38, no. 1 (2005): 142–47. http://dx.doi.org/10.3182/20050703-6-cz-1902.00024.
Full textLin, Lizhen, Niu Mu, Pokman Cheung, and David Dunson. "Extrinsic Gaussian Processes for Regression and Classification on Manifolds." Bayesian Analysis 14, no. 3 (September 2019): 887–906. http://dx.doi.org/10.1214/18-ba1135.
Full textZhu, Bin, and David B. Dunson. "Locally Adaptive Bayes Nonparametric Regression via Nested Gaussian Processes." Journal of the American Statistical Association 108, no. 504 (December 2013): 1445–56. http://dx.doi.org/10.1080/01621459.2013.838568.
Full textShi, J. Q., R. Murray-Smith, and D. M. Titterington. "Bayesian regression and classification using mixtures of Gaussian processes." International Journal of Adaptive Control and Signal Processing 17, no. 2 (2003): 149–61. http://dx.doi.org/10.1002/acs.744.
Full textXu, Bohan, Rayus Kuplicki, Sandip Sen, and Martin P. Paulus. "The pitfalls of using Gaussian Process Regression for normative modeling." PLOS ONE 16, no. 9 (September 15, 2021): e0252108. http://dx.doi.org/10.1371/journal.pone.0252108.
Full textDurrande, Nicolas, James Hensman, Magnus Rattray, and Neil D. Lawrence. "Detecting periodicities with Gaussian processes." PeerJ Computer Science 2 (April 13, 2016): e50. http://dx.doi.org/10.7717/peerj-cs.50.
Full textWang, Xiangyu, and J. Q. Sun. "Multi-stage regression fatigue analysis of non-Gaussian stress processes." Journal of Sound and Vibration 280, no. 1-2 (February 2005): 455–65. http://dx.doi.org/10.1016/j.jsv.2004.02.036.
Full textNguyen, Thi Nhat Anh, Abdesselam Bouzerdoum, and Son Lam Phung. "Stochastic variational hierarchical mixture of sparse Gaussian processes for regression." Machine Learning 107, no. 12 (July 6, 2018): 1947–86. http://dx.doi.org/10.1007/s10994-018-5721-5.
Full textBenavoli, Alessio, Dario Azzimonti, and Dario Piga. "Skew Gaussian processes for classification." Machine Learning 109, no. 9-10 (September 2020): 1877–902. http://dx.doi.org/10.1007/s10994-020-05906-3.
Full textZhu, Jinlin, Zhiqiang Ge, and Zhihuan Song. "Variational Bayesian Gaussian Mixture Regression for Soft Sensing Key Variables in Non-Gaussian Industrial Processes." IEEE Transactions on Control Systems Technology 25, no. 3 (May 2017): 1092–99. http://dx.doi.org/10.1109/tcst.2016.2576999.
Full textLiang, Junjie, Yanting Wu, Dongkuan Xu, and Vasant G. Honavar. "Longitudinal Deep Kernel Gaussian Process Regression." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 10 (May 18, 2021): 8556–64. http://dx.doi.org/10.1609/aaai.v35i10.17038.
Full textMcClintock, Thomas, and Eduardo Rozo. "Reconstructing probability distributions with Gaussian processes." Monthly Notices of the Royal Astronomical Society 489, no. 3 (September 2, 2019): 4155–60. http://dx.doi.org/10.1093/mnras/stz2426.
Full textСушникова, Д. А. "Application of block low-rank matrices in Gaussian processes for regression." Numerical Methods and Programming (Vychislitel'nye Metody i Programmirovanie), no. 3 (August 31, 2017): 214–20. http://dx.doi.org/10.26089/nummet.v18r319.
Full textBurnaev, E. V., M. E. Panov, and A. A. Zaytsev. "Regression on the basis of nonstationary Gaussian processes with Bayesian regularization." Journal of Communications Technology and Electronics 61, no. 6 (June 2016): 661–71. http://dx.doi.org/10.1134/s1064226916060061.
Full textYuan, Jin, Cheng-Liang Liu, Xuemei Liu, Kesheng Wang, and Tao Yu. "Incorporating prior model into Gaussian processes regression for WEDM process modeling." Expert Systems with Applications 36, no. 4 (May 2009): 8084–92. http://dx.doi.org/10.1016/j.eswa.2008.10.048.
Full textBurnaev, E. V., A. A. Zaytsev, and V. G. Spokoiny. "The Bernstein-von Mises theorem for regression based on Gaussian Processes." Russian Mathematical Surveys 68, no. 5 (October 31, 2013): 954–56. http://dx.doi.org/10.1070/rm2013v068n05abeh004863.
Full textVerrelst, Jochem, Juan Pablo Rivera, Anatoly Gitelson, Jesus Delegido, José Moreno, and Gustau Camps-Valls. "Spectral band selection for vegetation properties retrieval using Gaussian processes regression." International Journal of Applied Earth Observation and Geoinformation 52 (October 2016): 554–67. http://dx.doi.org/10.1016/j.jag.2016.07.016.
Full textdi Sciascio, Fernando, and Adriana N. Amicarelli. "Biomass estimation in batch biotechnological processes by Bayesian Gaussian process regression." Computers & Chemical Engineering 32, no. 12 (December 2008): 3264–73. http://dx.doi.org/10.1016/j.compchemeng.2008.05.015.
Full textLiu, Yiqi, Yarong Song, Jurg Keller, Philip Bond, and Guangming Jiang. "Prediction of concrete corrosion in sewers with hybrid Gaussian processes regression model." RSC Advances 7, no. 49 (2017): 30894–903. http://dx.doi.org/10.1039/c7ra03959j.
Full textTsay, Wen-Jen. "SPURIOUS REGRESSION BETWEEN I(1) PROCESSES WITH INFINITE VARIANCE ERRORS." Econometric Theory 15, no. 4 (August 1999): 622–28. http://dx.doi.org/10.1017/s0266466699154069.
Full textAlbert, Christopher G. "Gaussian Processes for Data Fulfilling Linear Differential Equations." Proceedings 33, no. 1 (November 21, 2019): 5. http://dx.doi.org/10.3390/proceedings2019033005.
Full textWei, San Xi, and Zong Hai Sun. "A Multi-Classification Method Based on Gaussian Processes." Applied Mechanics and Materials 198-199 (September 2012): 1333–37. http://dx.doi.org/10.4028/www.scientific.net/amm.198-199.1333.
Full textLu, Chi-Ken, and Patrick Shafto. "Conditional Deep Gaussian Processes: Multi-Fidelity Kernel Learning." Entropy 23, no. 11 (November 20, 2021): 1545. http://dx.doi.org/10.3390/e23111545.
Full textKomaki, Fumiyasu. "Homogeneous Gaussian Markov processes on general lattices." Advances in Applied Probability 28, no. 1 (March 1996): 189–206. http://dx.doi.org/10.2307/1427917.
Full textKomaki, Fumiyasu. "Homogeneous Gaussian Markov processes on general lattices." Advances in Applied Probability 28, no. 01 (March 1996): 189–206. http://dx.doi.org/10.1017/s0001867800027324.
Full textZhou, Le, Junghui Chen, and Zhihuan Song. "Recursive Gaussian Process Regression Model for Adaptive Quality Monitoring in Batch Processes." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/761280.
Full textHong, Xiaodan, Biao Huang, Yongsheng Ding, Fan Guo, Lei Chen, and Lihong Ren. "Multivariate Gaussian process regression for nonlinear modelling with colored noise." Transactions of the Institute of Measurement and Control 41, no. 8 (November 21, 2018): 2268–79. http://dx.doi.org/10.1177/0142331218798429.
Full textSamuelsson, Oscar, Anders Björk, Jesús Zambrano, and Bengt Carlsson. "Gaussian process regression for monitoring and fault detection of wastewater treatment processes." Water Science and Technology 75, no. 12 (March 25, 2017): 2952–63. http://dx.doi.org/10.2166/wst.2017.162.
Full textSofro, A’yunin, Jian Qing Shi, and Chunzheng Cao. "Regression analysis for multivariate process data of counts using convolved Gaussian processes." Journal of Statistical Planning and Inference 206 (May 2020): 57–74. http://dx.doi.org/10.1016/j.jspi.2019.09.005.
Full textYuan, Xiaofeng, Zhiqiang Ge, Hongwei Zhang, Zhihuan Song, and Peiliang Wang. "Soft Sensor for Multiphase and Multimode Processes Based on Gaussian Mixture Regression." IFAC Proceedings Volumes 47, no. 3 (2014): 1067–72. http://dx.doi.org/10.3182/20140824-6-za-1003.01752.
Full textTong, Chudong, Ting Lan, and Xuhua Shi. "Soft sensing of non-Gaussian processes using ensemble modified independent component regression." Chemometrics and Intelligent Laboratory Systems 157 (October 2016): 120–26. http://dx.doi.org/10.1016/j.chemolab.2016.07.006.
Full textMei, Congli, Yong Su, Guohai Liu, Yuhan Ding, and Zhiling Liao. "Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes." Chinese Journal of Chemical Engineering 25, no. 1 (January 2017): 116–22. http://dx.doi.org/10.1016/j.cjche.2016.07.005.
Full textYin, Yuehong, Ming Jun Ren, and Lijian Sun. "Dependant Gaussian processes regression for intelligent sampling of freeform and structured surfaces." CIRP Annals 66, no. 1 (2017): 511–14. http://dx.doi.org/10.1016/j.cirp.2017.04.063.
Full textPhan, Anh Tuan, Thi Tuyet Hong Vu, Dinh Quang Nguyen, Eleonora Riva Sanseverino, Hang Thi-Thuy Le, and Van Cong Bui. "Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network." Energies 15, no. 23 (December 4, 2022): 9190. http://dx.doi.org/10.3390/en15239190.
Full textMao, Runjun, Chengdong Cao, James Jing Yue Qian, Jiufan Wang, and Yunpeng Liu. "Mixture of Gaussian Processes Based on Bayesian Optimization." Journal of Sensors 2022 (September 15, 2022): 1–10. http://dx.doi.org/10.1155/2022/7646554.
Full textSundararajan, S., and S. S. Keerthi. "Predictive Approaches for Choosing Hyperparameters in Gaussian Processes." Neural Computation 13, no. 5 (May 1, 2001): 1103–18. http://dx.doi.org/10.1162/08997660151134343.
Full textTian, Jinkai, Peifeng Yan, and Da Huang. "Kernel Analysis Based on Dirichlet Processes Mixture Models." Entropy 21, no. 9 (September 2, 2019): 857. http://dx.doi.org/10.3390/e21090857.
Full textSollich, Peter, and Anason Halees. "Learning Curves for Gaussian Process Regression: Approximations and Bounds." Neural Computation 14, no. 6 (June 1, 2002): 1393–428. http://dx.doi.org/10.1162/089976602753712990.
Full textAgou, Vasiliki D., Andrew Pavlides, and Dionissios T. Hristopulos. "Spatial Modeling of Precipitation Based on Data-Driven Warping of Gaussian Processes." Entropy 24, no. 3 (February 23, 2022): 321. http://dx.doi.org/10.3390/e24030321.
Full textLi, Jin Hua, Shui Sheng Chen, and Wei Bing Sheng. "Conditional Simulation of No-Gaussian Stochastic Process Based on Neural Networks." Advanced Materials Research 479-481 (February 2012): 1959–62. http://dx.doi.org/10.4028/www.scientific.net/amr.479-481.1959.
Full textFiedler, Christian, Carsten W. Scherer, and Sebastian Trimpe. "Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 7439–47. http://dx.doi.org/10.1609/aaai.v35i8.16912.
Full text