Literatura académica sobre el tema "Linear estimation problems"
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Artículos de revistas sobre el tema "Linear estimation problems"
Florens, Jean-Pierre y Anna Simoni. "REGULARIZING PRIORS FOR LINEAR INVERSE PROBLEMS". Econometric Theory 32, n.º 1 (6 de noviembre de 2014): 71–121. http://dx.doi.org/10.1017/s0266466614000796.
Texto completodel Álamo, Miguel y Axel Munk. "Total variation multiscale estimators for linear inverse problems". Information and Inference: A Journal of the IMA 9, n.º 4 (2 de marzo de 2020): 961–86. http://dx.doi.org/10.1093/imaiai/iaaa001.
Texto completoRoss, G. J. S. "Estimation problems of non-linear functional relationships". Journal of Applied Statistics 17, n.º 3 (enero de 1990): 299–306. http://dx.doi.org/10.1080/02664769000000002.
Texto completoKoo, Ja-Yong y Han-Yeong Chung. "Log-density estimation in linear inverse problems". Annals of Statistics 26, n.º 1 (febrero de 1998): 335–62. http://dx.doi.org/10.1214/aos/1030563989.
Texto completoVolaufová, Júlia. "Some estimation problems in multistage linear models". Linear Algebra and its Applications 388 (septiembre de 2004): 389–97. http://dx.doi.org/10.1016/j.laa.2004.03.007.
Texto completoAdjali, M. H. y M. Laurent. "Thermal conductivity estimation in non-linear problems". International Journal of Heat and Mass Transfer 50, n.º 23-24 (noviembre de 2007): 4623–28. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2007.03.005.
Texto completoRan, Mengfei y Yihe Yang. "Optimal Estimation of Large Functional and Longitudinal Data by Using Functional Linear Mixed Model". Mathematics 10, n.º 22 (17 de noviembre de 2022): 4322. http://dx.doi.org/10.3390/math10224322.
Texto completoODEN, J. TINSLEY, SERGE PRUDHOMME, TIM WESTERMANN, JON BASS y MARK E. BOTKIN. "ERROR ESTIMATION OF EIGENFREQUENCIES FOR ELASTICITY AND SHELL PROBLEMS". Mathematical Models and Methods in Applied Sciences 13, n.º 03 (marzo de 2003): 323–44. http://dx.doi.org/10.1142/s0218202503002520.
Texto completoС. И., Носков, y Базилевский, М. П. "Multiple Lv-estimation of Linear Regression Models". Успехи кибернетики / Russian Journal of Cybernetics, n.º 4(12) (28 de diciembre de 2022): 32–40. http://dx.doi.org/10.51790/2712-9942-2022-3-4-04.
Texto completoEndtmayer, Bernhard, Ulrich Langer y Thomas Wick. "Multigoal-oriented error estimates for non-linear problems". Journal of Numerical Mathematics 27, n.º 4 (18 de diciembre de 2019): 215–36. http://dx.doi.org/10.1515/jnma-2018-0038.
Texto completoTesis sobre el tema "Linear estimation problems"
Edlund, Ove. "Solution of linear programming and non-linear regression problems using linear M-estimation methods /". Luleå, 1999. http://epubl.luth.se/1402-1544/1999/17/index.html.
Texto completoPIEROPAN, MIRKO. "Expectation Propagation Methods for Approximate Inference in Linear Estimation Problems". Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2918002.
Texto completoKaperick, Bryan James. "Diagonal Estimation with Probing Methods". Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/90402.
Texto completoMaster of Science
In the past several decades, as computational resources increase, a recurring problem is that of estimating certain properties very large linear systems (matrices containing real or complex entries). One particularly important quantity is the trace of a matrix, defined as the sum of the entries along its diagonal. In this thesis, we explore a problem that has only recently been studied, in estimating the diagonal entries of a particular matrix explicitly. For these methods to be computationally more efficient than existing methods, and with favorable convergence properties, we require the matrix in question to have a majority of its entries be zero (the matrix is sparse), with the largest-magnitude entries clustered near and on its diagonal, and very large in size. In fact, this thesis focuses on a class of methods called probing methods, which are of particular efficiency when the matrix is not known explicitly, but rather can only be accessed through matrix vector multiplications with arbitrary vectors. Our contribution is new analysis of these diagonal probing methods which extends the heavily-studied trace estimation problem, new applications for which probing methods are a natural choice for diagonal estimation, and a new class of deterministic probing methods which have favorable properties for large parallel computing architectures which are becoming ever-more-necessary as problem sizes continue to increase beyond the scope of single processor architectures.
Schülke, Christophe. "Statistical physics of linear and bilinear inference problems". Sorbonne Paris Cité, 2016. http://www.theses.fr/2016USPCC058.
Texto completoThe recent development of compressed sensing has led to spectacular advances in the under standing of sparse linear estimation problems as well as in algorithms to solve them. It has also triggered anew wave of developments in the related fields of generalized linear and bilinear inference problems. These problems have in common that they combine a linear mixing step and a nonlinear, probabilistic sensing step, producing indirect measurements of a signal of interest. Such a setting arises in problems such as medical or astronomical Imaging. The aim of this thesis is to propose efficient algorithms for this class of problems and to perform their theoretical analysis. To this end, it uses belief propagation, thanks to which high-dimensional distributions can be sampled efficiently, thus making a bayesian approach to inference tractable. The resulting algorithms undergo phase transitions that can be analyzed using the replica method, initially developed in statistical physics of disordered systems. The analysis reveals phases in which inference is easy, hard or impossible, corresponding to different energy landscapes of the problem. The main contributions of this thesis can be divided into three categories. First, the application of known algorithms to concrete problems : community detection, superposition codes and an innovative imaging system. Second, a new, efficient message-passing algorithm for blind sensor calibration, that could be used in signal processing for a large class of measurement systems. Third, a theoretical analysis of achievable performances in matrix compressed sensing and of instabilities in bayesian bilinear inference algorithms
Mattavelli, Marco Mattavelli Marco Mattavelli Marco. "Motion analysis and estimation : from III-posed discrete linear inverse problems to MPEG-2 coding /". Lausanne, 1997. http://library.epfl.ch/theses/?nr=1596.
Texto completoBarbier, Jean. "Statistical physics and approximate message-passing algorithms for sparse linear estimation problems in signal processing and coding theory". Sorbonne Paris Cité, 2015. http://www.theses.fr/2015USPCC130.
Texto completoThis thesis is interested in the application of statistical physics methods and inference to signal processing and coding theory, more precisely, to sparse linear estimation problems. The main tools are essentially the graphical models and the approximate message-passing algorithm together with the cavity method (referred as the state evolution analysis in the signal processing context) for its theoretical analysis. We will also use the replica method of statistical physics of disordered systems which allows to associate to the studied problems a cost function referred as the potential of free entropy in physics. It allows to predict the different phases of typical complexity of the problem as a function of external parameters such as the noise level or the number of measurements one has about the signal: the inference can be typically easy, hard or impossible. We will see that the hard phase corresponds to a regime of coexistence of the actual solution together with another unwanted solution of the message passing equations. In this phase, it represents a metastable state which is not the true equilibrium solution. This phenomenon can be linked to supercooled water blocked in the liquid state below its freezing critical temperature. Thanks to this understanding of blocking phenomenon of the algorithm, we will use a method that allows to overcome the metastability mimicing the strategy adopted by nature itself for supercooled water: the nucleation and spatial coupling. In supercooled water, a weak localized perturbation is enough to create a crystal nucleus that will propagate in all the medium thanks to the physical couplings between closeby atoms. The same process will help the algorithm to find the signal, thanks to the introduction of a nucleus containing local information about the signal. It will then spread as a "reconstruction wave" similar to the crystal in the water. After an introduction to statistical inference and sparse linear estimation, we will introduce the necessary tools. Then we will move to applications of these notions. They will be divided into two parts. The signal processing part will focus essentially on the compressed sensing problem where we seek to infer a sparse signal from a small number of linear projections of it that can be noisy. We will study in details the influence of structured operators instead of purely random ones used originally in compressed sensing. These allow a substantial gain in computational complexity and necessary memory allocation, which are necessary conditions in order to work with very large signals. We will see that the combined use of such operators with spatial coupling allows the implementation of an highly optimized algorithm able to reach near to optimal performances. We will also study the algorithm behavior for reconstruction of approximately sparse signals, a fundamental question for the application of compressed sensing to real life problems. A direct application will be studied via the reconstruction of images measured by fluorescence microscopy. The reconstruction of "natural" images will be considered as well. In coding theory, we will look at the message-passing decoding performances for two distincts real noisy channel models. A first scheme where the signal to infer will be the noise itself will be presented. The second one, the sparse superposition codes for the additive white Gaussian noise channel is the first example of error correction scheme directly interpreted as a structured compressed sensing problem. Here we will apply all the tools developed in this thesis for finally obtaining a very promising decoder that allows to decode at very high transmission rates, very close of the fundamental channel limit
Krishnan, Rajet. "Problems in distributed signal processing in wireless sensor networks". Thesis, Manhattan, Kan. : Kansas State University, 2009. http://hdl.handle.net/2097/1351.
Texto completoKontak, Max [Verfasser]. "Novel algorithms of greedy-type for probability density estimation as well as linear and nonlinear inverse problems / Max Kontak". Siegen : Universitätsbibliothek der Universität Siegen, 2018. http://d-nb.info/1157094554/34.
Texto completoPester, Cornelia. "A posteriori error estimation for non-linear eigenvalue problems for differential operators of second order with focus on 3D vertex singularities". Doctoral thesis, Berlin Logos-Verl, 2006. http://deposit.ddb.de/cgi-bin/dokserv?id=2806614&prov=M&dok_var=1&dok_ext=htm.
Texto completoPester, Cornelia. "A posteriori error estimation for non-linear eigenvalue problems for differential operators of second order with focus on 3D vertex singularities". Doctoral thesis, Logos Verlag Berlin, 2005. https://monarch.qucosa.de/id/qucosa%3A18520.
Texto completoLibros sobre el tema "Linear estimation problems"
Kontoghiorghes, Erricos John. Parallel algorithms for linear models: Numerical methods and estimation problems. Boston: Kluwer Academic, 2000.
Buscar texto completoHesselager, Ole. On the application of bootstrap in some empirical linear bayes estimation problems. Copenhagen: University of Copenhagen, 1988.
Buscar texto completoPester, Cornelia. A posteriori error estimation for non-linear eigenvalue problems for differential operators of second order with focus on 3D vertex singularities. Berlin: Logos-Verl., 2006.
Buscar texto completoM, Milanese, ed. Bounding approaches to system identification. New York: Plenum Press, 1996.
Buscar texto completo1975-, Sims Robert y Ueltschi Daniel 1969-, eds. Entropy and the quantum II: Arizona School of Analysis with Applications, March 15-19, 2010, University of Arizona. Providence, R.I: American Mathematical Society, 2011.
Buscar texto completoParallel Algorithms for Linear Models: Numerical Methods and Estimation Problems. Springer, 2011.
Buscar texto completoKontoghiorghes, Erricos. Parallel Algorithms for Linear Models: Numerical Methods and Estimation Problems. Springer London, Limited, 2012.
Buscar texto completoCardot, Hervé y Pascal Sarda. Functional Linear Regression. Editado por Frédéric Ferraty y Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.2.
Texto completoNakonechnyi, Oleksandr y Yuri Podlipenko. Guaranteed Estimation Problems in the Theory of Linear Ordinary Differential Equations with Uncertain Data. River Publishers, 2021.
Buscar texto completoNakonechnyi, Oleksandr y Yuri Podlipenko. Guaranteed Estimation Problems in the Theory of Linear Ordinary Differential Equations with Uncertain Data. River Publishers, 2022.
Buscar texto completoCapítulos de libros sobre el tema "Linear estimation problems"
Grafarend, Erik W. y Joseph L. Awange. "Special Problems of Algebraic Regression and Stochastic Estimation". En Linear and Nonlinear Models, 493–525. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22241-2_14.
Texto completoGriffith, Daniel A. y Jean H. P. Paelinck. "Linear Expenditure Systems and Related Estimation Problems". En Advanced Studies in Theoretical and Applied Econometrics, 201–13. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72553-6_17.
Texto completoGrafarend, Erik, Silvelyn Zwanzig y Joseph Awange. "Special Problems of Algebraic Regression and Stochastic Estimation". En Applications of Linear and Nonlinear Models, 499–531. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94598-5_14.
Texto completoPillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao y Lennart Ljung. "Regularization in Reproducing Kernel Hilbert Spaces for Linear System Identification". En Regularized System Identification, 247–311. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_7.
Texto completoDobra, Adrian, Stephen E. Fienberg, Alessandro Rinaldo, Aleksandra Slavkovic y Yi Zhou. "Algebraic Statistics and Contingency Table Problems: Log-Linear Models, Likelihood Estimation, and Disclosure Limitation". En Emerging Applications of Algebraic Geometry, 63–88. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-09686-5_3.
Texto completoNakonechnyi, O. y Y. Podlipenko. "Guaranteed Estimation of Parameters of Boundary Value Problems for Linear Ordinary Differential Equations with General Boundary Data". En Guaranteed Estimation Problems in the Theory of Linear Ordinary Differential Equations with Uncertain Data, 163–216. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338369-4.
Texto completoNakonechnyi, O. y Y. Podlipenko. "Guaranteed Estimation of Unknown Solutions and Right-Hand Sides of First Order Linear Systems of Periodic ODEs". En Guaranteed Estimation Problems in the Theory of Linear Ordinary Differential Equations with Uncertain Data, 79–102. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338369-2.
Texto completoNakonechnyi, O. y Y. Podlipenko. "Guaranteed Estimation of Solutions of Boundary Value Problems for Linear Ordinary Differential Equations with Decomposed Boundary Data". En Guaranteed Estimation Problems in the Theory of Linear Ordinary Differential Equations with Uncertain Data, 103–62. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338369-3.
Texto completoNakonechnyi, O. y Y. Podlipenko. "Guaranteed Estimates of Solutions and Right-Hand Sides of the Cauchy Problem Under Incomplete Data". En Guaranteed Estimation Problems in the Theory of Linear Ordinary Differential Equations with Uncertain Data, 5–77. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003338369-1.
Texto completoKorotov, Sergey, Pekka Neittaanmäki y Sergey Repin. "A Posteriori Error Estimation in Terms of Linear Functionals for Boundary Value Problems of Elliptic Type". En Numerical Mathematics and Advanced Applications, 587–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18775-9_56.
Texto completoActas de conferencias sobre el tema "Linear estimation problems"
Van Wijk, K., J. A. Scales y W. Navidi. "Uncertainty Estimation and Error Analysis for Linear Inversion Problems". En 63rd EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2001. http://dx.doi.org/10.3997/2214-4609-pdb.15.n-33.
Texto completoFuhrmann, Daniel R. "One-step optimal measurement selection for linear gaussian estimation problems". En 2007 International Waveform Diversity and Design Conference. IEEE, 2007. http://dx.doi.org/10.1109/wddc.2007.4339415.
Texto completoAnnaswamy, A. M., C. Thanomsat, N. R. Mehta y A. P. Loh. "A New Approach to Estimation of Nonlinear Parametrization in Dynamic Systems". En ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-0398.
Texto completoIkami, Daiki, Toshihiko Yamasaki y Kiyoharu Aizawa. "Fast and Robust Estimation for Unit-Norm Constrained Linear Fitting Problems". En 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00850.
Texto completoSuliman, Mohamed A., Houssem Sifaou, Tarig Ballal, Mohamed-Slim Alouini y Tareq Y. Al-Naffouri. "Robust Estimation in Linear ILL-Posed Problems with Adaptive Regularization Scheme". En ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462651.
Texto completoIto, Yoshimichi, Katsumi Irie y Shun Otsuka. "Estimation of geometric parameters in 3D reconstruction problems using linear matrix inequalities". En 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS). IEEE, 2014. http://dx.doi.org/10.1109/scis-isis.2014.7044790.
Texto completoVolkov, Vasiliy y Dmitriy Demyanov. "Optimal Estimation of the Linear Functional of State Variables of a Dynamic System". En 2019 XXI International Conference Complex Systems: Control and Modeling Problems (CSCMP). IEEE, 2019. http://dx.doi.org/10.1109/cscmp45713.2019.8976873.
Texto completoLiu, Zhaoqiang y Jun Han. "Projected Gradient Descent Algorithms for Solving Nonlinear Inverse Problems with Generative Priors". En Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/454.
Texto completoHill, David C. "Identification of Gas Turbine Dynamics: Time-Domain Estimation Problems". En ASME 1997 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/97-gt-031.
Texto completoChubich, Vladimir M. y Alina E. Prokofeva. "The Application of Robust Estimation to Active Parametric Identification of Stochastic Linear Discrete Systems". En 2018 XIV International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE). IEEE, 2018. http://dx.doi.org/10.1109/apeie.2018.8545985.
Texto completoInformes sobre el tema "Linear estimation problems"
Hou, Elizabeth Mary y Earl Christopher Lawrence. Variational Methods for Posterior Estimation of Non-linear Inverse Problems. Office of Scientific and Technical Information (OSTI), septiembre de 2018. http://dx.doi.org/10.2172/1475317.
Texto completoAyoul-Guilmard, Q., F. Nobile, S. Ganesh, M. Nuñez, R. Tosi, C. Soriano y R. Rosi. D5.5 Report on the application of multi-level Monte Carlo to wind engineering. Scipedia, 2022. http://dx.doi.org/10.23967/exaqute.2022.3.03.
Texto completoSearcy, Stephen W. y Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, agosto de 1993. http://dx.doi.org/10.32747/1993.7568747.bard.
Texto completoMayfield, Colin. Capacity Development in the Water Sector: the case of Massive Open On-line Courses. United Nations University Institute for Water, Environment and Health, enero de 2017. http://dx.doi.org/10.53328/mwud6984.
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