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Journal articles on the topic 'Gaussian process regression model'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Sofro, A., and A. Oktaviarina. "Gaussian Process Regression Model in Spatial Logistic Regression." Journal of Physics: Conference Series 947 (January 2018): 012005. http://dx.doi.org/10.1088/1742-6596/947/1/012005.

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2

Nguyen-Tuong, Duy, Matthias Seeger, and Jan Peters. "Model Learning with Local Gaussian Process Regression." Advanced Robotics 23, no. 15 (January 2009): 2015–34. http://dx.doi.org/10.1163/016918609x12529286896877.

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3

Wang, Bo, and Jian Qing Shi. "Generalized Gaussian Process Regression Model for Non-Gaussian Functional Data." Journal of the American Statistical Association 109, no. 507 (July 3, 2014): 1123–33. http://dx.doi.org/10.1080/01621459.2014.889021.

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4

Pearce, Robert, Peter Ireland, and Eduardo Romero. "Thermal matching using Gaussian process regression." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 234, no. 6 (January 28, 2020): 1172–80. http://dx.doi.org/10.1177/0954410020901961.

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Thermal matching is a key stage of the development process for a gas turbine engine where component models are verified to ensure the correct metal temperature distribution has been used in life calculations. The thermal match involves adjusting parameters of a thermal model in order to match an experimental temperature distribution, usually obtained from a thermal paint test. Current methodologies involve manually adjusting parameters, which is both time consuming and leads to variation in the matches achieved. This paper presents a new method to conduct thermal matching, where Gaussian process regression is utilised to obtain a surrogate model from which optimal parameters for matching are obtained. This standardised procedure removes subjectivity from the match and gives faster and more consistent matches. The method is introduced and demonstrated for a number of cases involving a leading edge impingement system that has been isolated from a high pressure turbine blade.

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Park, Chiwoo, David J. Borth, Nicholas S. Wilson, Chad N. Hunter, and Fritz J. Friedersdorf. "Robust Gaussian process regression with a bias model." Pattern Recognition 124 (April 2022): 108444. http://dx.doi.org/10.1016/j.patcog.2021.108444.

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Hewing, Lukas, Juraj Kabzan, and Melanie N. Zeilinger. "Cautious Model Predictive Control Using Gaussian Process Regression." IEEE Transactions on Control Systems Technology 28, no. 6 (November 2020): 2736–43. http://dx.doi.org/10.1109/tcst.2019.2949757.

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7

Bachoc, Francois, Fabrice Gamboa, Jean-Michel Loubes, and Nil Venet. "A Gaussian Process Regression Model for Distribution Inputs." IEEE Transactions on Information Theory 64, no. 10 (October 2018): 6620–37. http://dx.doi.org/10.1109/tit.2017.2762322.

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8

Misumi, Toshihiro. "MODEL SELECTION FOR FUNCTIONAL MIXED MODEL VIA GAUSSIAN PROCESS REGRESSION." Bulletin of informatics and cybernetics 46 (December 2014): 23–35. http://dx.doi.org/10.5109/1798144.

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9

Chapaneri, Santosh, and Deepak Jayaswal. "Structured Gaussian Process Regression of Music Mood." Fundamenta Informaticae 176, no. 2 (December 18, 2020): 183–203. http://dx.doi.org/10.3233/fi-2020-1970.

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Modeling the music mood has wide applications in music categorization, retrieval, and recommendation systems; however, it is challenging to computationally model the affective content of music due to its subjective nature. In this work, a structured regression framework is proposed to model the valence and arousal mood dimensions of music using a single regression model at a linear computational cost. To tackle the subjectivity phenomena, a confidence-interval based estimated consensus is computed by modeling the behavior of various annotators (e.g. biased, adversarial) and is shown to perform better than using the average annotation values. For a compact feature representation of music clips, variational Bayesian inference is used to learn the Gaussian mixture model representation of acoustic features and chord-related features are used to improve the valence estimation by probing the chord progressions between chroma frames. The dimensionality of features is further reduced using an adaptive version of kernel PCA. Using an efficient implementation of twin Gaussian process for structured regression, the proposed work achieves a significant improvement in R2 for arousal and valence dimensions relative to state-of-the-art techniques on two benchmark datasets for music mood estimation.

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Terry, 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.

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Gaussian processes offer a flexible kernel method for regression. While Gaussian processes have many useful theoretical properties and have proven practically useful, they suffer from poor scaling in the number of observations. In particular, the cubic time complexity of updating standard Gaussian process models can be a limiting factor in applications. We propose an algorithm for sequentially partitioning the input space and fitting a localized Gaussian process to each disjoint region. The algorithm is shown to have superior time and space complexity to existing methods, and its sequential nature allows the model to be updated efficiently. The algorithm constructs a model for which the time complexity of updating is tightly bounded above by a pre-specified parameter. To the best of our knowledge, the model is the first local Gaussian process regression model to achieve linear memory complexity. Theoretical continuity properties of the model are proven. We demonstrate the efficacy of the resulting model on several multi-dimensional regression tasks.

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Teng, Fei, Wenyuan Tao, and Chung-Ming Own. "Localization Reliability Improvement Using Deep Gaussian Process Regression Model." Sensors 18, no. 12 (November 27, 2018): 4164. http://dx.doi.org/10.3390/s18124164.

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With the widespread use of the Global Positioning System, indoor positioning technology has attracted increasing attention. Many systems with distinct deployment costs and positioning accuracies have been developed over the past decade for indoor positioning. The method that is based on received signal strength (RSS) is the most widely used. However, manually measuring RSS signal values to build a fingerprint database is costly and time-consuming, and it is impractical in a dynamic environment with a large positioning area. In this study, we propose an indoor positioning system that is based on the deep Gaussian process regression (DGPR) model. This model is a nonparametric model and it only needs to measure part of the reference points, thus reducing the time and cost required for data collection. The model converts the RSS values into four types of characterizing values as input data and then predicts the position coordinates using DGPR. Finally, after reinforcement learning, the position coordinates are optimized. The authors conducted several experiments on a simulated environment by MATLAB and physical environments at Tianjin University. The experiments examined different environments, different kernels, and positioning accuracy. The results showed that the proposed method could not only retain the positioning accuracy, but also save the computation time that is required for location estimation.

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12

Bayati, Abdolkhalegh, Kim-Khoa Nguyen, and Mohamed Cheriet. "Gaussian Process Regression Ensemble Model for Network Traffic Prediction." IEEE Access 8 (2020): 176540–54. http://dx.doi.org/10.1109/access.2020.3026337.

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13

Lin, Chaoning, Tongchun Li, Siyu Chen, Xiaoqing Liu, Chuan Lin, and Siling Liang. "Gaussian process regression-based forecasting model of dam deformation." Neural Computing and Applications 31, no. 12 (August 6, 2019): 8503–18. http://dx.doi.org/10.1007/s00521-019-04375-7.

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14

Gattiker, J. R., M. S. Hamada, D. M. Higdon, M. Schonlau, and W. J. Welch. "Using a Gaussian Process as a Nonparametric Regression Model." Quality and Reliability Engineering International 32, no. 2 (February 19, 2015): 673–80. http://dx.doi.org/10.1002/qre.1782.

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15

Hong, 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.

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Nonlinearity of process systems along with colored noises is common in chemical processes. A multivariate (multiple inputs and multiple outputs) Gaussian process regression (MGPR) modelling approach, which can model multivariate nonlinear processes, is developed in this paper. The developed GPR model considers the Gaussian colored noise, rather than the traditional Gaussian white noise. The colored noise is described by the moving average (MA) model and the autoregressive (AR) model, respectively, with unknown parameters so that a MA-GPR model and an AR-GPR model are developed. These two colored noise based models are further extended to the MGPR model to generate the MA-MGPR model and the AR-MGPR model. The covariance functions of the MA-MGPR model or the AR-MGPR model are formulated with consideration of the autocorrelation of noises. Moreover, all parameters are estimated by using a unidimensional updated particle swarm optimization (PSO) algorithm, simultaneously. Numerical examples as well as a three-level drawing model of Carbon fiber production process are used to demonstrate the effectiveness of the proposed modelling approaches.

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Ferkous, Khaled, Farouk Chellali, Abdalah Kouzou, and Belgacem Bekkar. "Wavelet-Gaussian Process Regression Model for Regression Daily Solar Radiation in Ghardaia, Algeria." Instrumentation Mesure Métrologie 20, no. 2 (April 30, 2021): 113–19. http://dx.doi.org/10.18280/i2m.200208.

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Several methods have been used to predict daily solar radiation in recent years, such as artificial intelligence and hybrid models. In this paper, a Wavelet coupled Gaussian Process Regression (W-GPR) model was proposed to predict the daily solar radiation received on a horizontal surface in Ghardaia (Algeria). A statistical period of four years (2013 -2016) was used where the first three years (2013-2015) are used to train model and the last year (2016) to test the model for predicting daily total solar radiation. Different types of wave mother and different combinations of input data were evaluated based on the minimum air temperature, relative humidity and extraterrestrial solar radiation on a horizontal surface. The results demonstrated the effectiveness of the new hybrid model W-GPR compared to the classical GPR model in terms of Root Mean Square Error (RMSE), relative Root Mean Square Error (rRMSE), Mean Absolute Error (MAE) and determination coefficient (R2).

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17

Li, Tanda, Guy R. Davies, Alexander J. Lyttle, Warrick H. Ball, Lindsey M. Carboneau, and Rafael A. García. "Modelling stars with Gaussian Process Regression: augmenting stellar model grid." Monthly Notices of the Royal Astronomical Society 511, no. 4 (February 21, 2022): 5597–610. http://dx.doi.org/10.1093/mnras/stac467.

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ABSTRACT Grid-based modelling is widely used for estimating stellar parameters. However, stellar model grid is sparse because of the computational cost. This paper demonstrates an application of a machine-learning algorithm using the Gaussian Process (GP) Regression that turns a sparse model grid on to a continuous function. We train GP models to map five fundamental inputs (mass, equivalent evolutionary phase, initial metallicity, initial helium fraction, and the mixing-length parameter) to observable outputs (effective temperature, surface gravity, radius, surface metallicity, and stellar age). We test the GP predictions for the five outputs using off-grid stellar models and find no obvious systematic offsets, indicating good accuracy in predictions. As a further validation, we apply these GP models to characterize 1000 fake stars. Inferred masses and ages determined with GP models well recover true values within one standard deviation. An important consequence of using GP-based interpolation is that stellar ages are more precise than those estimated with the original sparse grid because of the full sampling of fundamental inputs.

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18

Rius Carretero, David, and Salvador Torra Porras. "APLICACIONES ACTUARIALES MEDIANTE GAUSSIAN PROCESS REGRESSION: VIDA Y NO VIDA." Anales del Instituto de Actuarios Españoles, no. 28 (December 2022): 67–100. http://dx.doi.org/10.26360/2022_3.

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Resumen En este trabajo se ha realizado una breve introducción sobre la metodología Regresión de Proceso Gaussiano (GPR) y dos aplicaciones en el ámbito Actuarial. Por un lado, se ha realizado un ejercicio de interpolación sobre las tablas de mortalidad PASEM Unisex 2020, concluyendo que el GPR es una excelente herramienta de interpolación, y que nos permite una tarificación más ajustada en el ramo de Vida. Por otro lado, se ha integrado el GPR como medida de predicción de provisiones en los ramos de No-Vida, obteniendo unos resultados prometedores. Por último, se concluye que un GPR puede ser un instrumento útil, siempre y cuando, se realice una buena selección del Kernel y un correcto período de entrenamiento del modelo. Palabras clave: proceso gaussiano, normal multivariante, covarianza, Ciencias Actuariales, distribuciones. Abstract In this work, a brief introduction has been made on the Gaussian Process Regression (GPR) methodology and two applications in the Actuarial field. On the one hand, an interpolation exercise has been carried out on the PASEM Unisex 2020 mortality tables, concluding that the GPR is an excellent interpolation tool, and that it allows us a more adjusted pricing in the Life branch. On the other hand, the GPR has been integrated as a predictive measure for provisions in Non-Life branches, obtaining promising results. Finally, it is concluded that a GPR can be a useful instrument, as long as a good Kernel selection and a correct model training period are carried out. Keywords: gaussian process, multivariate normal, covariance, Actuarial Science, distributions.

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19

Zhang, Tianyi, Song Peng, Yang Jia, Junkai Sun, He Tian, and Chuliang Yan. "Slip Estimation Model for Planetary Rover Using Gaussian Process Regression." Applied Sciences 12, no. 9 (May 9, 2022): 4789. http://dx.doi.org/10.3390/app12094789.

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Monitoring the rover slip is important; however, a certain level of estimation uncertainty is inevitable. In this paper, we establish slip estimation models for China’s Mars rover, Zhurong, using Gaussian process regression (GPR). The model was able to predict not only the average value of the longitudinal (slip_x) and lateral slip (slip_y), but also the maximum possible value that slip_x and slip_y could reach. The training data were collected on two simulated soils, TYII-2 and JLU Mars-2, and the GA-BP algorithm was applied as a comparison. The analysis results demonstrated that the soil type and dataset source had a direct impact on the applicability of the slip model on Mars conditions. The properties of the Martian soil near the Zhurong landing site were closer to the JLU Mars-2 simulated soil. The proposed GPR model had high estimation accuracy and estimation potential in slip value, and a 95% confidence interval that the rover could reach during motion. This work was part of a research effort aimed at ensuring the safety of Zhurong. The slip value may be used in subsequent path tracking research, and the slip confidence interval will be able to help guide path planning.

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20

Chen, Baixi, Luming Shen, and Hao Zhang. "Gaussian Process Regression-Based Material Model for Stochastic Structural Analysis." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 7, no. 3 (September 2021): 04021025. http://dx.doi.org/10.1061/ajrua6.0001138.

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21

Kim, Jae Hoan, and Jaeyong Lee. "Identifiability of covariance kernels in the Gaussian process regression model." Journal of the Korean Data And Information Science Society 32, no. 6 (November 30, 2021): 1373–92. http://dx.doi.org/10.7465/jkdi.2021.32.6.1373.

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22

Pan, Tianhong, Yang Cai, and Shan Chen. "Development of an Engine Calibration Model Using Gaussian Process Regression." International Journal of Automotive Technology 22, no. 2 (March 4, 2021): 327–34. http://dx.doi.org/10.1007/s12239-021-0031-5.

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23

Feng, Jianshe, Xiaodong Jia, Haoshu Cai, Feng Zhu, Xiang Li, and Jay Lee. "Cross Trajectory Gaussian Process Regression Model for Battery Health Prediction." Journal of Modern Power Systems and Clean Energy 9, no. 5 (2021): 1217–26. http://dx.doi.org/10.35833/mpce.2019.000142.

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24

G. "A Gaussian Process Regression Model for the Traveling Salesman Problem." Journal of Computer Science 8, no. 10 (October 1, 2012): 1749–58. http://dx.doi.org/10.3844/jcssp.2012.1749.1758.

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25

Choi, Taeryon, Jian Q. Shi, and Bo Wang. "A Gaussian process regression approach to a single-index model." Journal of Nonparametric Statistics 23, no. 1 (March 2011): 21–36. http://dx.doi.org/10.1080/10485251003768019.

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26

Hong, Xia, Junbin Gao, Xinwei Jiang, and Chris J. Harris. "Estimation of Gaussian process regression model using probability distance measures." Systems Science & Control Engineering 2, no. 1 (October 31, 2014): 655–63. http://dx.doi.org/10.1080/21642583.2014.970731.

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27

Mangili, Francesca. "A prior near-ignorance Gaussian process model for nonparametric regression." International Journal of Approximate Reasoning 78 (November 2016): 153–71. http://dx.doi.org/10.1016/j.ijar.2016.07.005.

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Zhang, Le, Zhong Liu, Jian-qiang Zhang, and Xiong-wei Ren. "A genetic Gaussian process regression model based on memetic algorithm." Journal of Central South University 20, no. 11 (November 2013): 3085–93. http://dx.doi.org/10.1007/s11771-013-1832-0.

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Han, Jianan, Xiao-Ping Zhang, and Fang Wang. "Gaussian Process Regression Stochastic Volatility Model for Financial Time Series." IEEE Journal of Selected Topics in Signal Processing 10, no. 6 (September 2016): 1015–28. http://dx.doi.org/10.1109/jstsp.2016.2570738.

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Lee, Youngsaeng, and Jeong-Soo Park. "Model selection algorithm in Gaussian process regression for computer experiments." Communications for Statistical Applications and Methods 24, no. 4 (July 31, 2017): 383–96. http://dx.doi.org/10.5351/csam.2017.24.4.383.

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31

邓, 旭生. "Gaussian Process Regression Model in the Context of Complex Data." Statistics and Application 11, no. 05 (2022): 1194–201. http://dx.doi.org/10.12677/sa.2022.115123.

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32

Zheng, Bowen, Kaiping Yu, Shuaishuai Liu, and Rui Zhao. "Interval model updating using universal grey mathematics and Gaussian process regression model." Mechanical Systems and Signal Processing 141 (July 2020): 106455. http://dx.doi.org/10.1016/j.ymssp.2019.106455.

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Ahmad, Mahmood, Maaz Amjad, Ramez Al-Mansob, Paweł Kamiński, Piotr Olczak, Beenish Khan, and Arnold Alguno. "Prediction of Liquefaction-Induced Lateral Displacements Using Gaussian Process Regression." Applied Sciences 12, no. 4 (February 14, 2022): 1977. http://dx.doi.org/10.3390/app12041977.

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During severe earthquakes, liquefaction-induced lateral displacement causes significant damage to designed structures. As a result, geotechnical specialists must accurately estimate lateral displacement in liquefaction-prone areas in order to ensure long-term development. This research proposes a Gaussian Process Regression (GPR) model based on 247 post liquefaction in-situ free face ground conditions case studies for analyzing liquefaction-induced lateral displacement. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination, coefficient of correlation, Nash–Sutcliffe efficiency coefficient, root mean square error (RMSE), and ratio of the RMSE to the standard deviation of measured data. The developed GPR model predictive ability is compared to that of three other known models—evolutionary polynomial regression, artificial neural network, and multi-layer regression available in the literature. The results show that the GPR model can accurately learn complicated nonlinear relationships between lateral displacement and its influencing factors. A sensitivity analysis is also presented in this study to assess the effects of input parameters on lateral displacement.

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Liu, Yuxin, Ron Noomen, and Pieter Visser. "A Gravity Assist Mapping Based on Gaussian Process Regression." Journal of the Astronautical Sciences 68, no. 1 (March 2021): 248–72. http://dx.doi.org/10.1007/s40295-021-00246-3.

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AbstractWe develop a Gravity Assist Mapping to quantify the effects of a flyby in a two-dimensional circular restricted three-body situation based on Gaussian Process Regression (GPR). This work is inspired by the Keplerian Map and Flyby Map. The flyby is allowed to occur anywhere above 300 km altitude at the Earth in the system of Sun-(Earth+Moon)-spacecraft, whereas the Keplerian map is typically restricted to the cases outside the Hill sphere only. The performance of the GPR model and the influence of training samples (number and distribution) on the quality of the prediction of post-flyby orbital states are investigated. The information provided by this training set is used to optimize the hyper-parameters in the GPR model. The trained model can make predictions of the post-flyby state of an object with an arbitrary initial condition and is demonstrated to be efficient and accurate when evaluated against the results of numerical integration. The method can be attractive for space mission design.

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Lu, Ziwei, and Hongfei Yu. "Single Image Super-Resolution Algorithm based on Fixed-Point Multi-Model Gaussian Process Regression." Journal of Physics: Conference Series 2289, no. 1 (June 1, 2022): 012024. http://dx.doi.org/10.1088/1742-6596/2289/1/012024.

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Abstract To solve the problem of large amount of computation in matrix inversion of Gaussian process regression model, a super-resolution algorithm based on local Gaussian process regression of fixed point multi-model is proposed. Firstly, the training samples are classified by Gaussian mixture model, and image patches are randomly selected as fixed points in each type of training samples, and its K nearest neighbor patch are searched. Secondly, local Gaussian process regression model by using its low-mid-frequency components and the corresponding high frequency components. Again, low resolution test image patch is classified, only the K nearest neighbor test image patch is searched during reconstruction. And then, find the nearest fixed point in each kind of image patch, and use the local Gaussian process regression model based on the fixed point to predict its corresponding high-frequency component. Finally, corresponding high frequency information is predicted by using this trained model, which improves the reconstruction efficiency. Experimental results have demonstrated that the proposed algorithm is superior in both quantitative and qualitative aspects against other algorithms.

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Zi, Lingling, and Junping Du. "Energy-Driven Image Interpolation Using Gaussian Process Regression." Journal of Applied Mathematics 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/435924.

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Image interpolation, as a method of obtaining a high-resolution image from the corresponding low-resolution image, is a classical problem in image processing. In this paper, we propose a novel energy-driven interpolation algorithm employing Gaussian process regression. In our algorithm, each interpolated pixel is predicted by a combination of two information sources: first is a statistical model adopted to mine underlying information, and second is an energy computation technique used to acquire information on pixel properties. We further demonstrate that our algorithm can not only achieve image interpolation, but also reduce noise in the original image. Our experiments show that the proposed algorithm can achieve encouraging performance in terms of image visualization and quantitative measures.

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Xiao, Han, and Claudia Eckert. "Lazy Gaussian Process Committee for Real-Time Online Regression." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 30, 2013): 969–76. http://dx.doi.org/10.1609/aaai.v27i1.8572.

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A significant problem of Gaussian process (GP) is its unfavorable scaling with a large amount of data. To overcome this issue, we present a novel GP approximation scheme for online regression. Our model is based on a combination of multiple GPs with random hyperparameters. The model is trained by incrementally allocating new examples to a selected subset of GPs. The selection is carried out efficiently by optimizing a submodular function. Experiments on real-world data sets showed that our method outperforms existing online GP regression methods in both accuracy and efficiency. The applicability of the proposed method is demonstrated by the mouse-trajectory prediction in an Internet banking scenario.

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Zhang, Renhui, and Xutao Zhao. "Inverse Method of Centrifugal Pump Blade Based on Gaussian Process Regression." Mathematical Problems in Engineering 2020 (February 28, 2020): 1–10. http://dx.doi.org/10.1155/2020/4605625.

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The inverse problem is always one of the important issues in the field of fluid machinery for the complex relationship among the blade shape, the hydraulic performance, and the inner flow structure. Based on Bayesian theory of posterior probability obtained from known prior probability, the inverse methods for the centrifugal pump blade based on the single-output Gaussian process regression (SOGPR) and the multioutput Gaussian process regression (MOGPR) were proposed, respectively. The training sample set consists of the blade shape parameters and the distribution of flow parameters. The hyperparameters in the inverse problem models were trained by using the maximum likelihood estimation and the gradient descent algorithm. The blade shape corresponding to the objective blade load can be achieved by the trained inverse problem models. The MH48-12.5 low specific speed centrifugal pump was selected to verify the proposed inverse methods. The reliability and accuracy of both inverse problem models were confirmed and compared by implementing leave-one-out (LOO) cross-validation and extrapolation characteristic analysis. The results show that the blade shapes within the sample space can be reconstructed exactly by both models. The root mean square errors of the MOGPR inverse problem model for the pump blade are generally lower than those of the SOGPR inverse problem model in the LOO cross-validation. The extrapolation characteristic of the MOGPR inverse problem model is better than that of the SOGPR inverse problem model for the correlation between the blade shape parameters can be fully considered by the correlation matrix of the MOGPR model. The proposed inverse methods can efficiently solve the inverse problem of centrifugal pump blade with sufficient accuracy.

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Lei, Yu, Min Guo, Hongbing Cai, Dandan Hu, and Danning Zhao. "Prediction of Length-of-day Using Gaussian Process Regression." Journal of Navigation 68, no. 3 (January 19, 2015): 563–75. http://dx.doi.org/10.1017/s0373463314000927.

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The predictions of Length-Of-Day (LOD) are studied by means of Gaussian Process Regression (GPR). The EOP C04 time-series with daily values from the International Earth Rotation and Reference Systems Service (IERS) serve as the data basis. Firstly, well known effects that can be described by functional models, for example effects of the solid Earth and ocean tides or seasonal atmospheric variations, are removed a priori from the C04 time-series. Only the differences between the modelled and actual LOD, i.e. the irregular and quasi-periodic variations, are employed for training and prediction. Different input patterns are discussed and compared so as to optimise the GPR model. The optimal patterns have been found in terms of the prediction accuracy and efficiency, which conduct the multi-step ahead predictions utilising the formerly predicted values as inputs. Finally, the results of the predictions are analysed and compared with those obtained by other prediction methods. It is shown that the accuracy of the predictions are comparable with that of other prediction methods. The developed method is easy to use.

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40

Girolami, Mark, and Simon Rogers. "Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors." Neural Computation 18, no. 8 (August 2006): 1790–817. http://dx.doi.org/10.1162/neco.2006.18.8.1790.

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It is well known in the statistics literature that augmenting binary and polychotomous response models with gaussian latent variables enables exact Bayesian analysis via Gibbs sampling from the parameter posterior. By adopting such a data augmentation strategy, dispensing with priors over regression coefficients in favor of gaussian process (GP) priors over functions, and employing variational approximations to the full posterior, we obtain efficient computational methods for GP classification in the multiclass setting.1 The model augmentation with additional latent variables ensures full a posteriori class coupling while retaining the simple a priori independent GP covariance structure from which sparse approximations, such as multiclass informative vector machines (IVM), emerge in a natural and straightforward manner. This is the first time that a fully variational Bayesian treatment for multiclass GP classification has been developed without having to resort to additional explicit approximations to the nongaussian likelihood term. Empirical comparisons with exact analysis use Markov Chain Monte Carlo (MCMC) and Laplace approximations illustrate the utility of the variational approximation as a computationally economic alternative to full MCMC and it is shown to be more accurate than the Laplace approximation.

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41

Sajja, Sri Lavanya. "Online Sparse Gaussian Process (GP) Regression Model For Human Motion Tracking." IOSR Journal of Engineering 02, no. 10 (October 2012): 17–22. http://dx.doi.org/10.9790/3021-021021722.

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Fan, Kesen, and Yiming Wan. "State-of-Charge Dependent Battery Model Identification Using Gaussian Process Regression." IFAC-PapersOnLine 54, no. 3 (2021): 647–52. http://dx.doi.org/10.1016/j.ifacol.2021.08.315.

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Suphawan, Kamonrat, Ruethaichanok Kardkasem, and Kuntalee Chaisee. "A Gaussian Process Regression Model for Forecasting Stock Exchange of Thailand." Trends in Sciences 19, no. 6 (March 3, 2022): 3045. http://dx.doi.org/10.48048/tis.2022.3045.

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Abstract:

A stock price index measures the change in several share prices, which can describe the market and assist investors in deciding on a specific investment. Thus, foreseeing the stock price index benefits investors in creating a better investment strategy. However, forecasting the stock price index can be challenging due to its non-linearity, non-stationary and high uncertainty. Gaussian process regression (GPR) is an attractive and powerful approach for prediction, especially when the data fluctuates over time with fewer restrictions. Besides, the GPR gains advantages over other forecasting techniques as it can offer predictions with uncertainty to provide margin errors. In this study, we evaluate the use of GPR to predict the stock price of Thailand (SET). The SET data are divided into 2 datasets; the data in the year 2015 - 2020 and the data in the year 2020 due to the massive change during the COVID-19 pandemic. The prediction results from the GPR are then compared to the machine learning approaches, artificial neural network (ANN) and recurrent neural network (RNN) using evaluation scores; the root mean square error (RMSE), the mean absolute error (MAE), the mean absolute percentage error (MAPE) and the Nash-Sutcliffe efficiency (NSE). The results indicate that the GPR is superior to the ANN and RNN for both datasets as it provides a high prediction accuracy. Moreover, the results suggest that the GPR is less sensitive to the number of input lags in the model. Therefore, the GPR is more favorable for the prediction of SET than the ANN and RNN. HIGHLIGHTS The Gaussian process regression (GPR) was applied to predict the stock price index of Thailand (SET) The predictive performance of the GPR was compared to artificial neural networks (ANNs) and recurrent neural networks (RNNs) The results indicate that GPR outperformed the other methods as it provided a high prediction accuracy along with prediction intervals GRAPHICAL ABSTRACT

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Park, Kyusic, and Matthew S. Allen. "A Gaussian process regression reduced order model for geometrically nonlinear structures." Mechanical Systems and Signal Processing 184 (February 2023): 109720. http://dx.doi.org/10.1016/j.ymssp.2022.109720.

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Guo, Wei, Tianhong Pan, Zhengming Li, and Shan Chen. "Model Calibration Method for Soft Sensors Using Adaptive Gaussian Process Regression." IEEE Access 7 (2019): 168436–43. http://dx.doi.org/10.1109/access.2019.2954158.

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Rui, Jianwen, Hongbing Zhang, Quan Ren, Lizhi Yan, Qiang Guo, and Dailu Zhang. "TOC content prediction based on a combined Gaussian process regression model." Marine and Petroleum Geology 118 (August 2020): 104429. http://dx.doi.org/10.1016/j.marpetgeo.2020.104429.

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Gijsberts, Arjan, and Giorgio Metta. "Real-time model learning using Incremental Sparse Spectrum Gaussian Process Regression." Neural Networks 41 (May 2013): 59–69. http://dx.doi.org/10.1016/j.neunet.2012.08.011.

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Yuan, 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.

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Beckers, Thomas, Jonas Umlauft, and Sandra Hirche. "Stable Model-based Control with Gaussian Process Regression for Robot Manipulators." IFAC-PapersOnLine 50, no. 1 (July 2017): 3877–84. http://dx.doi.org/10.1016/j.ifacol.2017.08.359.

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Park, J., D. Lechevalier, R. Ak, M. Ferguson, K. H. Law, Y. T. T. Lee, and S. Rachuri. "Gaussian Process Regression (GPR) Representation in Predictive Model Markup Language (PMML)." Smart and Sustainable Manufacturing Systems 1, no. 1 (March 29, 2017): 20160008. http://dx.doi.org/10.1520/ssms20160008.

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