Добірка наукової літератури з теми "Constrained Gaussian processes"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Constrained Gaussian processes".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Constrained Gaussian processes":
Wang, Xiaojing, and James O. Berger. "Estimating Shape Constrained Functions Using Gaussian Processes." SIAM/ASA Journal on Uncertainty Quantification 4, no. 1 (January 2016): 1–25. http://dx.doi.org/10.1137/140955033.
Graf, Siegfried, and Harald Luschgy. "Entropy-constrained functional quantization of Gaussian processes." Proceedings of the American Mathematical Society 133, no. 11 (May 2, 2005): 3403–9. http://dx.doi.org/10.1090/s0002-9939-05-07888-3.
Niu, Mu, Pokman Cheung, Lizhen Lin, Zhenwen Dai, Neil Lawrence, and David Dunson. "Intrinsic Gaussian processes on complex constrained domains." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 81, no. 3 (April 19, 2019): 603–27. http://dx.doi.org/10.1111/rssb.12320.
Girbés-Juan, Vicent, Joaquín Moll, Antonio Sala, and Leopoldo Armesto. "Cautious Bayesian Optimization: A Line Tracker Case Study." Sensors 23, no. 16 (August 18, 2023): 7266. http://dx.doi.org/10.3390/s23167266.
Yang, Shihao, Samuel W. K. Wong, and S. C. Kou. "Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes." Proceedings of the National Academy of Sciences 118, no. 15 (April 9, 2021): e2020397118. http://dx.doi.org/10.1073/pnas.2020397118.
Rattunde, Leonhard, Igor Laptev, Edgar D. Klenske, and Hans-Christian Möhring. "Safe optimization for feedrate scheduling of power-constrained milling processes by using Gaussian processes." Procedia CIRP 99 (2021): 127–32. http://dx.doi.org/10.1016/j.procir.2021.03.020.
Schweidtmann, Artur M., Dominik Bongartz, Daniel Grothe, Tim Kerkenhoff, Xiaopeng Lin, Jaromił Najman, and Alexander Mitsos. "Deterministic global optimization with Gaussian processes embedded." Mathematical Programming Computation 13, no. 3 (June 25, 2021): 553–81. http://dx.doi.org/10.1007/s12532-021-00204-y.
Li, Ming, Xiafei Tang, Qichun Zhang, and Yiqun Zou. "Non-Gaussian Pseudolinear Kalman Filtering-Based Target Motion Analysis with State Constraints." Applied Sciences 12, no. 19 (October 4, 2022): 9975. http://dx.doi.org/10.3390/app12199975.
Salmon, John. "Generation of Correlated and Constrained Gaussian Stochastic Processes for N-Body Simulations." Astrophysical Journal 460 (March 1996): 59. http://dx.doi.org/10.1086/176952.
Rocher, Antoine, Vanina Ruhlmann-Kleider, Etienne Burtin, and Arnaud de Mattia. "Halo occupation distribution of Emission Line Galaxies: fitting method with Gaussian processes." Journal of Cosmology and Astroparticle Physics 2023, no. 05 (May 1, 2023): 033. http://dx.doi.org/10.1088/1475-7516/2023/05/033.
Дисертації з теми "Constrained Gaussian processes":
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.
The thesis is divided into three main parts, we will summarize the major contributions of the thesis as follows.Low complexity Gaussian processes:Gaussian process regression usually scales as $O(n^3)$ for computation and $O(n^2)$ for memory requirements, where $n$ represents the number of observations. This limitation becomes unfeasible for many problems when $n$ is large. In this thesis, we investigate the Karhunen-Loève expansion of Gaussian processes, which offers several advantages over low-rank compression techniques. By truncating the Karhunen-Loève expansion, we obtain an explicit low-rank approximation of the covariance matrix (Gram matrix), greatly simplifying statistical inference when the number of truncations is small relative to $n$.We then provide explicit solutions for low complexity Gaussian processes. We seek Karhunen-Loève expansions, by solving for eigenpaires of a differential operator where the covariance function serves as the Green function. We offer explicit solutions for the Matérn differential operator and for differential operators with eigenfunctions represented by classical polynomials. In the experimental section, we compare our proposed methods with alternative approaches, revealing their enhanced capability in capturing intricate patterns.Constrained Gaussian processes:This thesis introduces a novel approach used constrained Gaussian processes to approximate a density function based on observations. To address these constraints, our approach involves modeling square root of unknown density function realized as a Gaussian process. In this work, we adopt a truncated version of the Karhunen-Loève expansion as the approximation method. A notable advantage of this approach is that the coefficients are Gaussian and independent, with the constraints on the realized functions entirely dictated by the constraints on the random coefficients. After conditioning on both available data and constraints, the posterior distribution of the coefficients is a normal distribution constrained to the unit sphere. This distribution poses analytical intractability, necessitating numerical methods for approximation. To this end, this thesis employs spherical Hamiltonian Monte Carlo (HMC). The efficacy of the proposed framework is validated through a series of experiments, with performance comparisons against alternative methods.Transfer learning on the manifold of finite probability measures:Finally, we introduce transfer learning models in the space of finite probability measures, denoted as $mathcal{P}_+(I)$. In our investigation, we endow the space $mathcal{P}_+(I)$ with the Fisher-Rao metric, transforming it into a Riemannian manifold. This Riemannian manifold, $mathcal{P}_+(I)$, holds a significant place in Information Geometry and has numerous applications. Within this thesis, we provide detailed formulas for geodesics, the exponential map, the log map, and parallel transport on $mathcal{P}_+(I)$.Our exploration extends to statistical models situated within $mathcal{P}_+(I)$, typically conducted within the tangent space of this manifold. With a comprehensive set of geometric tools, we introduce transfer learning models facilitating knowledge transfer between these tangent spaces. Detailed algorithms for transfer learning encompassing Principal Component Analysis (PCA) and linear regression models are presented. To substantiate these concepts, we conduct a series of experiments, offering empirical evidence of their efficacy
Lu, Zhengdong. "Constrained clustering and cognitive decline detection /." Full text open access at:, 2008. http://content.ohsu.edu/u?/etd,650.
Cothren, Jackson D. "Reliability in constrained Gauss-Markov models an analytical and differential approach with applications in photogrammetry /." Connect to this title online, 2004. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1085689960.
Title from first page of PDF file. Document formatted into pages; contains xii, 119 p.; also includes graphics (some col.). Includes bibliographical references (p. 106-109). Available online via OhioLINK's ETD Center
Lopez, lopera Andres Felipe. "Gaussian Process Modelling under Inequality Constraints." Thesis, Lyon, 2019. https://tel.archives-ouvertes.fr/tel-02863891.
Conditioning Gaussian processes (GPs) by inequality constraints gives more realistic models. This thesis focuses on the finite-dimensional approximation of GP models proposed by Maatouk (2015), which satisfies the constraints everywhere in the input space. Several contributions are provided. First, we study the use of Markov chain Monte Carlo methods for truncated multinormals. They result in efficient sampling for linear inequality constraints. Second, we explore the extension of the model, previously limited up tothree-dimensional spaces, to higher dimensions. The introduction of a noise effect allows us to go up to dimension five. We propose a sequential algorithm based on knot insertion, which concentrates the computational budget on the most active dimensions. We also explore the Delaunay triangulation as an alternative to tensorisation. Finally, we study the case of additive models in this context, theoretically and on problems involving hundreds of input variables. Third, we give theoretical results on inference under inequality constraints. The asymptotic consistency and normality of maximum likelihood estimators are established. The main methods throughout this manuscript are implemented in R language programming.They are applied to risk assessment problems in nuclear safety and coastal flooding, accounting for positivity and monotonicity constraints. As a by-product, we also show that the proposed GP approach provides an original framework for modelling Poisson processes with stochastic intensities
Brahmantio, Bayu Beta. "Efficient Sampling of Gaussian Processes under Linear Inequality Constraints." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176246.
Maatouk, Hassan. "Correspondance entre régression par processus Gaussien et splines d'interpolation sous contraintes linéaires de type inégalité. Théorie et applications." Thesis, Saint-Etienne, EMSE, 2015. http://www.theses.fr/2015EMSE0791/document.
This thesis is dedicated to interpolation problems when the numerical function is known to satisfy some properties such as positivity, monotonicity or convexity. Two methods of interpolation are studied. The first one is deterministic and is based on convex optimization in a Reproducing Kernel Hilbert Space (RKHS). The second one is a Bayesian approach based on Gaussian Process Regression (GPR) or Kriging. By using a finite linear functional decomposition, we propose to approximate the original Gaussian process by a finite-dimensional Gaussian process such that conditional simulations satisfy all the inequality constraints. As a consequence, GPR is equivalent to the simulation of a truncated Gaussian vector to a convex set. The mode or Maximum A Posteriori is defined as a Bayesian estimator and prediction intervals are quantified by simulation. Convergence of the method is proved and the correspondence between the two methods is done. This can be seen as an extension of the correspondence established by [Kimeldorf and Wahba, 1971] between Bayesian estimation on stochastic process and smoothing by splines. Finally, a real application in insurance and finance is given to estimate a term-structure curve and default probabilities
Wang, Wayne. "Non-colliding Gaussian Process Regressions." Thesis, 2020. http://hdl.handle.net/1885/209109.
"Interval linear constraint solving in constraint logic programming." Chinese University of Hong Kong, 1994. http://library.cuhk.edu.hk/record=b5888548.
Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.
Includes bibliographical references (leaves 97-103).
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- Related Work --- p.2
Chapter 1.2 --- Organizations of the Dissertation --- p.4
Chapter 1.3 --- Notations --- p.4
Chapter 2 --- Overview of ICLP(R) --- p.6
Chapter 2.1 --- Basics of Interval Arithmetic --- p.6
Chapter 2.2 --- Relational Interval Arithmetic --- p.8
Chapter 2.2.1 --- Interval Reduction --- p.8
Chapter 2.2.2 --- Arithmetic Primitives --- p.10
Chapter 2.2.3 --- Interval Narrowing and Interval Splitting --- p.13
Chapter 2.3 --- Syntax and Semantics --- p.16
Chapter 3 --- Limitations of Interval Narrowing --- p.18
Chapter 3.1 --- Computation Inefficiency --- p.18
Chapter 3.2 --- Inability to Detect Inconsistency --- p.23
Chapter 3.3 --- The Newton Language --- p.27
Chapter 4 --- Design of CIAL --- p.30
Chapter 4.1 --- The CIAL Architecture --- p.30
Chapter 4.2 --- The Inference Engine --- p.31
Chapter 4.2.1 --- Interval Variables --- p.31
Chapter 4.2.2 --- Extended Unification Algorithm --- p.33
Chapter 4.3 --- The Solver Interface and Constraint Decomposition --- p.34
Chapter 4.4 --- The Linear and the Non-linear Solvers --- p.37
Chapter 5 --- The Linear Solver --- p.40
Chapter 5.1 --- An Interval Gaussian Elimination Solver --- p.41
Chapter 5.1.1 --- Naive Interval Gaussian Elimination --- p.41
Chapter 5.1.2 --- Generalized Interval Gaussian Elimination --- p.43
Chapter 5.1.3 --- Incrementality of Generalized Gaussian Elimination --- p.47
Chapter 5.1.4 --- Solvers Interaction --- p.50
Chapter 5.2 --- An Interval Gauss-Seidel Solver --- p.52
Chapter 5.2.1 --- Interval Gauss-Seidel Method --- p.52
Chapter 5.2.2 --- Preconditioning --- p.55
Chapter 5.2.3 --- Increment ality of Preconditioned Gauss-Seidel Method --- p.58
Chapter 5.2.4 --- Solver Interaction --- p.71
Chapter 5.3 --- Comparisons --- p.72
Chapter 5.3.1 --- Time Complexity --- p.72
Chapter 5.3.2 --- Storage Complexity --- p.73
Chapter 5.3.3 --- Others --- p.74
Chapter 6 --- Benchmarkings --- p.76
Chapter 6.1 --- Mortgage --- p.78
Chapter 6.2 --- Simple Linear Simultaneous Equations --- p.79
Chapter 6.3 --- Analysis of DC Circuit --- p.80
Chapter 6.4 --- Inconsistent Simultaneous Equations --- p.82
Chapter 6.5 --- Collision Problem --- p.82
Chapter 6.6 --- Wilkinson Polynomial --- p.85
Chapter 6.7 --- Summary and Discussion --- p.86
Chapter 6.8 --- Large System of Simultaneous Equations --- p.87
Chapter 6.9 --- Comparisons Between the Incremental and the Non-Incremental Preconditioning --- p.89
Chapter 7 --- Concluding Remarks --- p.93
Chapter 7.1 --- Summary and Contributions --- p.93
Chapter 7.2 --- Future Work --- p.95
Bibliography --- p.97
Koyejo, Oluwasanmi Oluseye. "Constrained relative entropy minimization with applications to multitask learning." 2013. http://hdl.handle.net/2152/20793.
text
"Bayesian-Entropy Method for Probabilistic Diagnostics and Prognostics of Engineering Systems." Doctoral diss., 2020. http://hdl.handle.net/2286/R.I.62900.
Dissertation/Thesis
Doctoral Dissertation Mechanical Engineering 2020
Частини книг з теми "Constrained Gaussian processes":
Arcucci, Rossella, Douglas McIlwraith, and Yi-Ke Guo. "Scalable Weak Constraint Gaussian Processes." In Lecture Notes in Computer Science, 111–25. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22747-0_9.
Kottakki, Krishna Kumar, Sharad Bhartiya, and Mani Bhushan. "Optimization Based Constrained Unscented Gaussian Sum Filter." In 12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering, 1715–20. Elsevier, 2015. http://dx.doi.org/10.1016/b978-0-444-63577-8.50131-5.
Sood, Anoop Kumar. "Finite Element-Based Optimization of Additive Manufacturing Process Using Statistical Modelling and League of Champion Algorithm." In Machine Learning Applications in Non-Conventional Machining Processes, 215–34. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3624-7.ch014.
Mei, Yongsheng, Tian Lan, Mahdi Imani, and Suresh Subramaniam. "A Bayesian Optimization Framework for Finding Local Optima in Expensive Multimodal Functions." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230455.
Pawlowsky-Glahn, Vera, and Richardo A. Olea. "Cokriging." In Geostatistical Analysis of Compositional Data. Oxford University Press, 2004. http://dx.doi.org/10.1093/oso/9780195171662.003.0011.
Arsenio, Artur Miguel. "Intelligent Approaches for Adaptation and Distribution of Personalized Multimedia Content." In Intelligent Multimedia Technologies for Networking Applications, 197–224. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2833-5.ch008.
Тези доповідей конференцій з теми "Constrained Gaussian processes":
Traganitis, Panagiotis A., and Georgios B. Giannakis. "Constrained Clustering using Gaussian Processes." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287331.
Swiler, Laura, Mamikon Gulian, Ari Frankel, Cosmin Safta, and John Jakeman. "Constrained Gaussian Processes: A Survey." In Proposed for presentation at the SIAM Computational Science and Engineering Conference 2021 held March 1 - February 5, 2021 in virtual. US DOE, 2021. http://dx.doi.org/10.2172/1847480.
Jetton, Cole, Chengda Li, and Christopher Hoyle. "Constrained Bayesian Optimization Methods Using Regression and Classification Gaussian Processes As Constraints." In ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/detc2023-109993.
Gulian, Mamikon. "Gaussian Process Regression Constrained by Boundary Value Problems." In Proposed for presentation at the IMSI Workshop: Expressing and Exploiting Structure in Modeling, Theory, and Computation with Gaussian Processes in ,. US DOE, 2022. http://dx.doi.org/10.2172/2004489.
Zinnen, Andreas, and Thomas Engel. "Deadline constrained scheduling in hybrid clouds with Gaussian processes." In Simulation (HPCS). IEEE, 2011. http://dx.doi.org/10.1109/hpcsim.2011.5999837.
Kang, Hyuk, and F. C. Park. "Configuration space learning for constrained manipulation tasks using Gaussian processes." In 2014 IEEE-RAS 14th International Conference on Humanoid Robots (Humanoids 2014). IEEE, 2014. http://dx.doi.org/10.1109/humanoids.2014.7041500.
Diwale, Sanket Sanjay, Ioannis Lymperopoulos, and Colin N. Jones. "Optimization of an Airborne Wind Energy system using constrained Gaussian Processes." In 2014 IEEE Conference on Control Applications (CCA). IEEE, 2014. http://dx.doi.org/10.1109/cca.2014.6981519.
Tran, Anh, Kathryn Maupin, and Theron Rodgers. "Integrated Computational Materials Engineering With Monotonic Gaussian Processes." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-89213.
Choi, Sungjoon, Kyungjae Lee, and Songhwai Oh. "Robust learning from demonstration using leveraged Gaussian processes and sparse-constrained optimization." In 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016. http://dx.doi.org/10.1109/icra.2016.7487168.
Mitrovic, Mile, Aleksandr Lukashevich, Petr Vorobev, Vladimir Terzija, Yury Maximov, and Deepjyoti Deka. "Fast Data-Driven Chance Constrained AC-OPF Using Hybrid Sparse Gaussian Processes." In 2023 IEEE Belgrade PowerTech. IEEE, 2023. http://dx.doi.org/10.1109/powertech55446.2023.10202724.
Звіти організацій з теми "Constrained Gaussian processes":
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), September 2020. http://dx.doi.org/10.2172/1668928.