Literatura académica sobre el tema "Gaussian inputs"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "Gaussian inputs".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "Gaussian inputs"
Hartman, Eric y James D. Keeler. "Predicting the Future: Advantages of Semilocal Units". Neural Computation 3, n.º 4 (diciembre de 1991): 566–78. http://dx.doi.org/10.1162/neco.1991.3.4.566.
Texto completoSrivastava, Ankur, Arun K. Subramaniyan y Liping Wang. "Analytical global sensitivity analysis with Gaussian processes". Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, n.º 3 (agosto de 2017): 235–50. http://dx.doi.org/10.1017/s0890060417000142.
Texto completoKoukoulas, P. y N. Kalouptsidis. "Nonlinear system identification using Gaussian inputs". IEEE Transactions on Signal Processing 43, n.º 8 (1995): 1831–41. http://dx.doi.org/10.1109/78.403342.
Texto completoSchwartz, Odelia, Terrence J. Sejnowski y Peter Dayan. "Soft Mixer Assignment in a Hierarchical Generative Model of Natural Scene Statistics". Neural Computation 18, n.º 11 (noviembre de 2006): 2680–718. http://dx.doi.org/10.1162/neco.2006.18.11.2680.
Texto completoWong, P. W. y R. M. Gray. "Sigma-delta modulation with i.i.d. Gaussian inputs". IEEE Transactions on Information Theory 36, n.º 4 (julio de 1990): 784–98. http://dx.doi.org/10.1109/18.53738.
Texto completoXu-Friedman, Matthew A. y Wade G. Regehr. "Dynamic-Clamp Analysis of the Effects of Convergence on Spike Timing. II. Few Synaptic Inputs". Journal of Neurophysiology 94, n.º 4 (octubre de 2005): 2526–34. http://dx.doi.org/10.1152/jn.01308.2004.
Texto completoCARD, HOWARD C. "STOCHASTIC RADIAL BASIS FUNCTIONS". International Journal of Neural Systems 11, n.º 02 (abril de 2001): 203–10. http://dx.doi.org/10.1142/s0129065701000552.
Texto completoMena-Parra, J., K. Bandura, M. A. Dobbs, J. R. Shaw y S. Siegel. "Quantization Bias for Digital Correlators". Journal of Astronomical Instrumentation 07, n.º 02n03 (septiembre de 2018): 1850008. http://dx.doi.org/10.1142/s2251171718500083.
Texto completoXu-Friedman, Matthew A. y Wade G. Regehr. "Dynamic-Clamp Analysis of the Effects of Convergence on Spike Timing. I. Many Synaptic Inputs". Journal of Neurophysiology 94, n.º 4 (octubre de 2005): 2512–25. http://dx.doi.org/10.1152/jn.01307.2004.
Texto completoBachoc, Francois, Fabrice Gamboa, Jean-Michel Loubes y Nil Venet. "A Gaussian Process Regression Model for Distribution Inputs". IEEE Transactions on Information Theory 64, n.º 10 (octubre de 2018): 6620–37. http://dx.doi.org/10.1109/tit.2017.2762322.
Texto completoTesis sobre el tema "Gaussian inputs"
Le, Anh Duc. "Fundamental Limits of Communication Channels under Non-Gaussian Interference". University of Akron / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=akron1469011496.
Texto completoCuesta, Ramirez Jhouben Janyk. "Optimization of a computationally expensive simulator with quantitative and qualitative inputs". Thesis, Lyon, 2022. http://www.theses.fr/2022LYSEM010.
Texto completoIn this thesis, costly mixed problems are approached through gaussian processes where the discrete variables are relaxed into continuous latent variables. the continuous space is more easily harvested by classical bayesian optimization techniques than a mixed space would. discrete variables are recovered either subsequently to the continuous optimization, or simultaneously with an additional continuous-discrete compatibility constraint that is handled with augmented lagrangians. several possible implementations of such bayesian mixed optimizers are compared. in particular, the reformulation of the problem with continuous latent variables is put in competition with searches working directly in the mixed space. among the algorithms involving latent variables and an augmented lagrangian, a particular attention is devoted to the lagrange multipliers for which a local and a global estimation techniques are studied. the comparisons are based on the repeated optimization of three analytical functions and a mechanical application regarding a beam design. an additional study for applying a proposed mixed optimization strategy in the field of mixed self-calibration is made. this analysis was inspired in an application in radionuclide quantification, which defined an specific inverse function that required the study of its multiple properties in the continuous scenario. a proposition of different deterministic and bayesian strategies was made towards a complete definition in a mixed variable setup
Zhang, Yulei. "Computer Experiments with Both Quantitative and Qualitative Inputs". The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1408042133.
Texto completoBetancourt, José. "Functional-input metamodeling : an application to coastal flood early warning". Thesis, Toulouse 3, 2020. http://www.theses.fr/2020TOU30097.
Texto completoCurrently, floods in general affect more people than any other hazard. In just the last decade of the 20th century, more than 1.5 billion were affected. In the seek to mitigate the impact of this type of hazard, strong scientific effort has been devoted to the constitution of computer codes that could be used as risk management tools. Available computer models now allow properly modelling coastal flooding events at a fairly high resolution. Unfortunately, their use is strongly prohibitive for early warning, with a simulation of few hours of maritime dynamics taking several hours to days of processing time, even on multi-processor clusters. This thesis is part of the ANR RISCOPE project, which aims at addressing this limitation by means of surrogate modeling of the hydrodynamic computer codes. As a particular requirement of this application, the metamodel should be able to deal with functional inputs corresponding to time varying maritime conditions. To this end, we focused on Gaussian process metamodels, originally developed for scalar inputs, but now available also for functional inputs. The nature of the inputs gave rise to a number of questions about the proper way to represent them in the metamodel: (i) which functional inputs are worth keeping as predictors, (ii) which dimension reduction method (e.g., B-splines, PCA, PLS) is ideal, (iii) which is a suitable projection dimension, and given our choice to work with Gaussian process metamodels, also the question of (iv) which is a convenient distance to measure similarities between functional input points within the kernel function. Some of these characteristics - hereon called structural parameters - of the model and some others such as the family of kernel (e.g., Gaussian, Matérn 5/2) are often arbitrarily chosen a priori. Sometimes, those are selected based on other studies. As one may intuit and has been shown by us through experiments, those decisions could have a strong impact on the prediction capability of the resulting model. Thus, without losing sight of our final goal of contributing to the improvement of coastal flooding early warning, we undertook the construction of an efficient methodology to set up the structural parameters of the model. As a first solution, we proposed an exploration approach based on the Response Surface Methodology. It was effectively used to tune the metamodel for an analytic toy function, as well as for a simplified version of the code studied in RISCOPE. While relatively simple, the proposed methodology was able to find metamodel configurations of high prediction capability with savings of up to 76.7% and 38.7% of the time spent by an exhaustive search approach in the analytic case and coastal flooding case, respectively. The solution found by our methodology was optimal in most cases. We developed later a second prototype based on Ant Colony Optimization (ACO). This new approach is more powerful in terms of solution time and flexibility in the features of the model allowed to be explored.[...]
Ptáček, Martin. "Spatial Function Estimation with Uncertain Sensor Locations". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-449288.
Texto completoSarkar, Avik. "The Capacity Region of the Gaussian Z-Interference Channel with Gaussian Input and Weak Interference". Thesis, North Dakota State University, 2016. https://hdl.handle.net/10365/27981.
Texto completoZhang, Boya. "Computer Experimental Design for Gaussian Process Surrogates". Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99886.
Texto completoDoctor of Philosophy
With a rapid development of computing power, computer experiments have gained popularity in various scientific fields, like cosmology, ecology and engineering. However, some computer experiments for complex processes are still computationally demanding. Thus, a statistical model built upon input-output observations, i.e., a so-called surrogate model or emulator, is needed as a fast substitute for the simulator. Design of experiments, i.e., how to select samples from the input space under budget constraints, is also worth studying. This dissertation focuses on the design problem under Gaussian process (GP) surrogates. The first work demonstrates empirically that commonly-used space-filling designs disappoint when the model hyperparameterization is unknown, and must be estimated from data observed at the chosen design sites. Thereafter, a new family of distance-based designs are proposed and their superior performance is illustrated in both static (design points are allocated at one shot) and sequential settings (data are sampled sequentially). The second contribution is motivated by a stochastic computer simulator of delta smelt conservation. This simulator is developed to assist in a study of delta smelt life cycles and to understand sensitivities to myriad natural variables and human interventions. However, the input space is high-dimensional, running the simulator is time-consuming, and its outputs change nonlinearly in both mean and variance. An innovative batch sequential design method is proposed, generalizing one-at-a-time sequential design to one-batch-at-a-time scheme with the goal of parallel computing. The criterion for subsequent data acquisition is carefully engineered to favor selection of replicates which boost statistical and computational efficiencies. The design performance is illustrated on a range of toy examples before embarking on a smelt simulation campaign and downstream input sensitivity analysis.
Ralston, Jonathon Carey. "Identification of a class of nonlinear systems in the non-Gaussian input case". Thesis, Queensland University of Technology, 1996. https://eprints.qut.edu.au/36000/2/36000_Digitised_Thesis.pdf.
Texto completoKim, Nungsoo. "Extraction of the second-order nonlinear response from model test data in random seas and comparison of the Gaussian and non-Gaussian models". Texas A&M University, 2004. http://hdl.handle.net/1969.1/3183.
Texto completoHan, Gang. "Modeling the output from computer experiments having quantitative and qualitative input variables and its applications". Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1228326460.
Texto completoLibros sobre el tema "Gaussian inputs"
Petersen, William B. Inpuff 2.0--a multiple source Gaussian puff dispersion algorithm user's guide. Research Triangle Park, NC: U.S. Environmental Protection Agency, Atmospheric Sciences Research Laboratory, 1987.
Buscar texto completoPetersen, William B. Inpuff 2.0--a multiple source Gaussian puff dispersion algorithm user's guide. Research Triangle Park, NC: U.S. Environmental Protection Agency, Atmospheric Sciences Research Laboratory, 1987.
Buscar texto completoPetersen, William B. Inpuff 2.0--a multiple source Gaussian puff dispersion algorithm user's guide. Research Triangle Park, NC: U.S. Environmental Protection Agency, Atmospheric Sciences Research Laboratory, 1987.
Buscar texto completoPetersen, William B. Inpuff 2.0--a multiple source Gaussian puff dispersion algorithm user's guide. Research Triangle Park, NC: U.S. Environmental Protection Agency, Atmospheric Sciences Research Laboratory, 1987.
Buscar texto completoPetersen, William B. Inpuff 2.0--a multiple source Gaussian puff dispersion algorithm user's guide. Research Triangle Park, NC: U.S. Environmental Protection Agency, Atmospheric Sciences Research Laboratory, 1987.
Buscar texto completoGary, Rosen I. y Institute for Computer Applications in Science and Engineering., eds. Approximation of discrete-time LQG compensators for distributed systems with boundary input and unbounded measurement. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1987.
Buscar texto completoLee, Herbert K. H., Matthew Taddy, Robert Gramacy y Genetha Gray. Designing and analysing a circuit device experiment using treed Gaussian processes. Editado por Anthony O'Hagan y Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.28.
Texto completoCapítulos de libros sobre el tema "Gaussian inputs"
Mandjes, Michel. "Queueing Networks with Gaussian Inputs". En International Series in Operations Research & Management Science, 531–60. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6472-4_12.
Texto completoBock, Sebastian, Philipp Schwarz y Martin G. Weiß. "U-Shape Phenomenon with Gaussian Noise and Clipped Inputs". En Proceedings of Eighth International Congress on Information and Communication Technology, 569–79. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3043-2_45.
Texto completoTomczak, Jakub M. "Gaussian Process Regression with Categorical Inputs for Predicting the Blood Glucose Level". En Advances in Intelligent Systems and Computing, 98–108. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48944-5_10.
Texto completoWyber, R. J. "The Design of Optimal Processors for Arrays with Non-Gaussian Noise Inputs". En Adaptive Methods in Underwater Acoustics, 515–26. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-5361-1_46.
Texto completoHashemi, Vahid, Jan Křetínský, Stefanie Mohr y Emmanouil Seferis. "Gaussian-Based Runtime Detection of Out-of-distribution Inputs for Neural Networks". En Runtime Verification, 254–64. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88494-9_14.
Texto completoBijak, Jakub y Jason Hilton. "Uncertainty Quantification, Model Calibration and Sensitivity". En Towards Bayesian Model-Based Demography, 71–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83039-7_5.
Texto completoCook, A., O. Rondon, J. Graindorge y G. Booth. "Iterative Gaussianisation for Multivariate Transformation". En Springer Proceedings in Earth and Environmental Sciences, 21–35. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_2.
Texto completoVinokur, Igor y David Tolpin. "Warped Input Gaussian Processes for Time Series Forecasting". En Lecture Notes in Computer Science, 205–20. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78086-9_16.
Texto completoMuscolino, G. "Response of Linear and Non-Linear Structural Systems under Gaussian or Non-Gaussian Filtered Input". En Dynamic Motion: Chaotic and Stochastic Behaviour, 203–99. Vienna: Springer Vienna, 1993. http://dx.doi.org/10.1007/978-3-7091-2682-0_6.
Texto completoSandberg, Irwin W. "Approximation of Input-Output Maps using Gaussian Radial Basis Functions". En Stability and Control of Dynamical Systems with Applications, 155–66. Boston, MA: Birkhäuser Boston, 2003. http://dx.doi.org/10.1007/978-1-4612-0037-6_8.
Texto completoActas de conferencias sobre el tema "Gaussian inputs"
Rodrigues, Miguel R. D., Fernando Perez-Cruz y Sergio Verduy. "Multiple-input multiple-output Gaussian channels: Optimal covariance for non-Gaussian inputs". En 2008 IEEE Information Theory Workshop (ITW). IEEE, 2008. http://dx.doi.org/10.1109/itw.2008.4578704.
Texto completoCao, Wei, Alex Dytso, Michael Fauss, Gang Feng y H. Vincent Poor. "Robust Waterfilling for Approximately Gaussian Inputs". En GLOBECOM 2019 - 2019 IEEE Global Communications Conference. IEEE, 2019. http://dx.doi.org/10.1109/globecom38437.2019.9013311.
Texto completoDytso, Alex, Natasha Devroye y Daniela Tuninetti. "On Gaussian interference channels with mixed gaussian and discrete inputs". En 2014 IEEE International Symposium on Information Theory (ISIT). IEEE, 2014. http://dx.doi.org/10.1109/isit.2014.6874835.
Texto completoGunduz, Deniz y Miquel Payaro. "Gaussian two-way relay channel with arbitrary inputs". En 2010 IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC 2010). IEEE, 2010. http://dx.doi.org/10.1109/pimrc.2010.5671657.
Texto completoRodrigues, Miguel R. D., Anelia Somekh-Baruch y Matthieu Bloch. "On Gaussian wiretap channels with M-PAM inputs". En 2010 European Wireless Conference (EW). IEEE, 2010. http://dx.doi.org/10.1109/ew.2010.5483475.
Texto completoNielsen, Jens Brehm, Bjorn Sand Jensen y Jan Larsen. "Pseudo inputs for pairwise learning with Gaussian processes". En 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2012. http://dx.doi.org/10.1109/mlsp.2012.6349812.
Texto completoRodrigues, Miguel R. D. y Gil Ramos. "On multiple-input multiple-output Gaussian channels with arbitrary inputs subject to jamming". En 2009 IEEE International Symposium on Information Theory - ISIT. IEEE, 2009. http://dx.doi.org/10.1109/isit.2009.5206051.
Texto completoLaradji, Issam H., Mark Schmidt, Vladimir Pavlovic y Minyoung Kim. "Efficient Deep Gaussian Process Models for Variable-Sized Inputs". En 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019. http://dx.doi.org/10.1109/ijcnn.2019.8851768.
Texto completoFergie, Martin y Aphrodite Galata. "Local Gaussian Processes for Pose Recognition from Noisy Inputs". En British Machine Vision Conference 2010. British Machine Vision Association, 2010. http://dx.doi.org/10.5244/c.24.98.
Texto completoPrakriya, Shankar, Subbarayan Pasupathy y Dimitrios Hatzinakos. "Blind identification of nonlinear models with non-Gaussian inputs". En Photonics East '95, editado por Raghuveer M. Rao, Soheil A. Dianat, Steven W. McLaughlin y Martin Hassner. SPIE, 1995. http://dx.doi.org/10.1117/12.228233.
Texto completoInformes sobre el tema "Gaussian inputs"
Pearson, Ken y Channing Arndt. Implementing Systematic Sensitivity Analysis Using GEMPACK. GTAP Technical Paper, noviembre de 2000. http://dx.doi.org/10.21642/gtap.tp03.
Texto completoTsidylo, Ivan M., Serhiy O. Semerikov, Tetiana I. Gargula, Hanna V. Solonetska, Yaroslav P. Zamora y Andrey V. Pikilnyak. Simulation of intellectual system for evaluation of multilevel test tasks on the basis of fuzzy logic. CEUR Workshop Proceedings, junio de 2021. http://dx.doi.org/10.31812/123456789/4370.
Texto completo