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Автор: Grafiati
Опубліковано: 2 березня 2024

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Статті в журналах з теми "Multiparameter estimation"

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Miyazaki, Jisho, and Keiji Matsumoto. "Imaginarity-free quantum multiparameter estimation." Quantum 6 (March 10, 2022): 665. http://dx.doi.org/10.22331/q-2022-03-10-665.

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Анотація:
Multiparameter quantum estimation is made difficult by the following three obstacles. First, incompatibility among different physical quantities poses a limit on the attainable precision. Second, the ultimate precision is not saturated until you discover the optimal measurement. Third, the optimal measurement may generally depend on the target values of parameters, and thus may be impossible to perform for unknown target states. We present a method to circumvent these three obstacles. A class of quantum statistical models, which utilizes antiunitary symmetries or, equivalently, real density matrices, offers compatible multiparameter estimations. The symmetries accompany the target-independent optimal measurements for pure-state models. Based on this finding, we propose methods to implement antiunitary symmetries for quantum metrology schemes. We further introduce a function which measures antiunitary asymmetry of quantum statistical models as a potential tool to characterize quantumness of phase transitions.
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2

Jensen, D. R. "Vector efficiency in multiparameter estimation." Linear Algebra and its Applications 151 (June 1991): 143–55. http://dx.doi.org/10.1016/0024-3795(91)90360-9.

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3

Alam, Khursheed, and Amitava Mitra. "Component risk in multiparameter estimation." Annals of the Institute of Statistical Mathematics 38, no. 3 (December 1986): 399–410. http://dx.doi.org/10.1007/bf02482526.

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4

Datta, Animesh, Rafał Demkowicz-Dobrzański, and Jing Liu. "Quantum multiparameter estimation and metrology—preface." Journal of Physics A: Mathematical and Theoretical 54, no. 46 (October 26, 2021): 460301. http://dx.doi.org/10.1088/1751-8121/ac0d6b.

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5

Kragten, J., P. C. de Jagher, and L. G. Decnop-Weever. "Non-linear multiparameter estimation in spectrophotometry." Analytica Chimica Acta 180 (1986): 457–72. http://dx.doi.org/10.1016/0003-2670(86)80025-3.

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6

Gorgucci, Eugenio, V. Chandrasekar, and Gianfranco Scarchilli. "Radar and Surface Measurement of Rainfall during CaPE: 26 July 1991 Case Study." Journal of Applied Meteorology 34, no. 7 (July 1, 1995): 1570–77. http://dx.doi.org/10.1175/1520-0450-34.7.1570.

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Abstract Conventional usage of multiparameter radar measurements for rainfall estimation has been associated with tracking the variability of the raindrop size distribution. The use of multiparameter radar measurements in a statistical framework to estimate rainfall is presented in this paper. The techniques developed in this paper are applied to the radar and rain gauge measurement of rainfall observed on 26 July 1991, during the Convection and Precipitation Electrification program. Conventional pointwise estimates of rainfall are also compared. The probability matching procedure, when applied to the radar and surface measurements shows that multiparameter radar algorithms can match the probability distribution functions better than the reflectivity based algorithms, thereby indicating the potential of multiparameter radar measurements for statistical approach to rainfall estimation.
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7

Cimini, Valeria, Ludovica Ruggiero, Ilaria Gianani, Marco Sbroscia, Tecla Gasperi, Emanuele Roccia, Luca Mancino, et al. "Multiparameter Approach to Dynamic Quantum Phase Estimation." Proceedings 12, no. 1 (December 4, 2019): 55. http://dx.doi.org/10.3390/proceedings2019012055.

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We have applied techniques of quantum phase estimation to the dynamical tracking of the optical activity of a solution of sucrose undergoing acid hydrolysis. We adopt a multiparameter approach that makes the estimation reliable and robust against setup instabilities.
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8

Gianani, Ilaria, and Claudia Benedetti. "Multiparameter estimation of continuous-time quantum walk Hamiltonians through machine learning." AVS Quantum Science 5, no. 1 (March 2023): 014405. http://dx.doi.org/10.1116/5.0137398.

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The characterization of the Hamiltonian parameters defining a quantum walk is of paramount importance when performing a variety of tasks, from quantum communication to computation. When dealing with physical implementations of quantum walks, the parameters themselves may not be directly accessible, and, thus, it is necessary to find alternative estimation strategies exploiting other observables. Here, we perform the multiparameter estimation of the Hamiltonian parameters characterizing a continuous-time quantum walk over a line graph with n-neighbor interactions using a deep neural network model fed with experimental probabilities at a given evolution time. We compare our results with the bounds derived from estimation theory and find that the neural network acts as a nearly optimal estimator both when the estimation of two or three parameters is performed.
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9

Liu, Jing, Haidong Yuan, Xiao-Ming Lu, and Xiaoguang Wang. "Quantum Fisher information matrix and multiparameter estimation." Journal of Physics A: Mathematical and Theoretical 53, no. 2 (December 18, 2019): 023001. http://dx.doi.org/10.1088/1751-8121/ab5d4d.

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Karlen, Walter, S. Raman, J. M. Ansermino, and G. A. Dumont. "Multiparameter Respiratory Rate Estimation From the Photoplethysmogram." IEEE Transactions on Biomedical Engineering 60, no. 7 (July 2013): 1946–53. http://dx.doi.org/10.1109/tbme.2013.2246160.

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Дисертації з теми "Multiparameter estimation"

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Chen, Lu-Hung, and 陳律閎. "On Estimation Methods in Generalized Multiparameter Likelihood Model." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/58043974660130747347.

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Анотація:
博士
臺灣大學
數學研究所
98
Multiparameter likelihood models (MLMs) with multiple covariates have a wide range of applications; however, they encounter the “curse of dimension- ality” problem when the dimension of the covariates is large. We develop a generalized multiparameter likelihood model that copes with multiple covari- ates and adapts to dynamic structural changes well. It includes some popular models, such as the partially linear and varying-coefficients models, as special cases. We discuss the backfitting and profile likelihood procedures and present a simple, effective two-step method to estimate both the parametric and the nonparametric components when the model is fixed. All these estimators of the parametric component has the n−1/2 convergence rate, and the estimator of the nonparametric component enjoys an adaptivity property. We suggest a data-driven procedure for selecting the bandwidths, and propose an initial estimator in backfitting and profile likelihood estimation of the parametric part to ensure stability of the approach in general settings. We further develop an automatic procedure to identify constant parameters in the underlying model. We provide several simulation studies and an application to infant mortality data of China to demonstrate the performance of our proposed method.
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2

Minkoff, Susan Elizabeth. "Multiparameter inversion and energy source estimation for a reflection seismic experiment." Thesis, 1995. http://hdl.handle.net/1911/16858.

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Анотація:
Reflection seismologists illuminate the subsurface by introducing energy into the ground. These propagating waves encounter heterogeneities in the subsurface material and are partly reflected back up to the surface where they are recorded as seismograms. The seismic energy source in most cases cannot be reliably measured in a laboratory but must be accurately estimated to allow one to extract the physical parameters which characterize the subsurface (such as velocity and density). The source and multiple earth parameters may be simultaneously successfully estimated by inversion. When the seismogram model is the plane-wave convolutional model derived from the constant density, variable sound velocity acoustic wave equation, perturbations in the seismic data stably determine perturbations in the source and reflectivity (the high-frequency relative fluctuation in the velocity). The stability of this determination improves as the angular range over which the data is defined increases. A more realistic model for wave propagation in the earth is the plane-wave convolutional model derived from the viscoelastic wave equation. Waveform inversion applied to field data from the Gulf of Mexico successfully estimates the long-wavelength compressional velocity, three elastic parameter reflectivities, and the anisotropic seismic source. The resulting reflectivities match measured well log data and agree with commonly-accepted lithological relationships. These inversion results predict 70% of the total seismic data and 90% of the data in an interval around the gas sand target. The resolution matrix measures how close inversion-estimated reflectivities are to the true parameters which generated the data and is useful when independent information such as well logs is unavailable. However, computing the resolution matrix from the singular value decomposition of the forward map (the usual technique) is prohibitive for real seismic inverse problems. Instead we approximate the resolution matrix from Lanczos estimates of the eigenvectors of the normal matrix. The resolution matrix indicates that our inversion-estimated source provides well resolved reflectivities in the depth interval of interest.
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Lu, Jin-Mei, and 呂靜梅. "Efficiency of maximum likelihood estimator in multiparameter survival model." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/11124319392573960959.

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Анотація:
碩士
國立中央大學
數學系
85
Le Cam 提出正則估計式(regular estimator)的概念,我們在本文討 論最大概似估計量(MLE)在多維參數及隨機設限(random censoriny)模式 下的正則有效性(regular efficiency) 假設可觀察到的數據是隨機設 限數據.我們以古典機率論為工具.在適當的條件下.證明了真實參數之MLE 的函數具有一致性,漸近常態性,以及對數概似函數的局部漸近常態性( LAN),也證明了真實參數之MLE的函數是正則估計式.同時,我們證明了關於 真實參數之MLE的漸近變異數(asymjptotic variance)最小,據此說明在此 模型下MLE的正則有效性.
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Частини книг з теми "Multiparameter estimation"

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Devroye, Luc, and Gábor Lugosi. "Multiparameter Kernel Estimates." In Combinatorial Methods in Density Estimation, 118–33. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0125-7_12.

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Müller, Hans-Georg, and Kathy Prewitt. "Applications of Multiparameter Weak Convergence for Adaptive Nonparametric Curve Estimation." In Nonparametric Functional Estimation and Related Topics, 141–66. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3222-0_11.

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3

Guo, Kunqi, Lixin Sun, Yong Lee, and Shilou Jia. "Cognitive Radio Access Based on Multiparameter Matching Ability Estimation." In Communications in Computer and Information Science, 405–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24999-0_56.

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4

Magiera, Ryszard. "Optimal Sequential Estimation Procedures Under Delayed Observations From Multiparameter Exponential Families." In Operations Research Proceedings, 200–205. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-58891-4_31.

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5

"Multiparameter Problems, in Particular Joint Estimation of Location and Scale." In Robust Statistics, 127–52. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471725250.ch6.

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6

TAKEUCHI, KEI, and MASAFUMI AKAHIRA. "ASYMPTOTIC OPTIMALITY OF THE GENERALIZED BAYES ESTIMATOR IN MULTIPARAMETER CASES." In Joint Statistical Papers of Akahira and Takeuchi, 170–82. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812791221_0013.

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7

TAKEUCHI, Kei, and Masafumi AKAHIRA. "Third Order Asymptotic Efficiency of Maximum Likelihood Estimator for Multiparameter Exponential Case." In Joint Statistical Papers of Akahira and Takeuchi, 101–23. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812791221_0008.

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Тези доповідей конференцій з теми "Multiparameter estimation"

1

G. Birgin, Ernesto, Ricardo Biloti, Martin Tygel, and Lucio T. Santos. "A New Algorithm For Traveltime Multiparameter Estimation." In 6th International Congress of the Brazilian Geophysical Society. European Association of Geoscientists & Engineers, 1999. http://dx.doi.org/10.3997/2214-4609-pdb.215.sbgf301.

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2

Shi, Jiancheng. "Estimation of snow properties using multiparameter SAR." In Third International Asia-Pacific Environmental Remote Sensing Remote Sensing of the Atmosphere, Ocean, Environment, and Space, edited by Christian D. Kummerow, JingShang Jiang, and Seiho Uratuka. SPIE, 2003. http://dx.doi.org/10.1117/12.466603.

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3

You, Chenglong, Sushovit Adhikari, Yuxi Chi, Margarite L. LaBorde, Corey T. Matyas, Chenyu Zhang, Zuen Su, et al. "Multiparameter estimation with single photons—Linearly-optically generated quantum entanglement beats the shotnoise limit." In JSAP-OSA Joint Symposia. Washington, D.C.: Optica Publishing Group, 2017. http://dx.doi.org/10.1364/jsap.2017.7p_a410_4.

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Анотація:
It was previously shown that optical networks using only single photon Fock states, passive linear optics, and single photon detection could achieve post-classical sensitivity for singleparameter estimation when the mode number is small. [1] Furthermore, it was shown that multiparameter estimation could achieve similar results with number-resolving detection. [2] Here, we consider an analogous architecture to the Quantum Fourier Transform Interferometer(QuFTI) proposed in [1] for the estimation of multiple phases simultaneously. Thus, we will see that this yields post-classical sensitivity for multiparameter estimation even for an asymptotically large number of modes. This system is also considered for nondeterministic photon sources and a variety of measurement schemes.
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4

Krueper, Gregory, Lior Cohen, Robert Mellors, Stephen B. Libby, Michael Messerly, Joshua Combes, and Juliet T. Gopinath. "Quantum Multiparameter Estimation Model of Cascaded Phases in Optical Fiber." In CLEO: Applications and Technology. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/cleo_at.2023.jth2a.17.

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Анотація:
We present a new architecture for quantum-enhanced multiparameter estimation, where measured phases are cascaded along a single optical fiber. Embedded reflectors separate these phases, enabling novel fiber-based quantum distributed sensing of temperature and strain.
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5

Albarelli, Francesco, and Rafal Demkowicz-Dobrzanski. "Probe Incompatibility in Multiparameter Noisy Quantum Channel Estimation." In Quantum Information and Measurement. Washington, D.C.: OSA, 2021. http://dx.doi.org/10.1364/qim.2021.w2a.1.

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6

Cimini, Valeria, Mauro Valeri, Emanuele Polino, Simone Piacentini, Francesco Hoch, Gabriele Bizzarri, Francesco Ceccarelli, et al. "Adaptive multiparameter estimation at the quantum bound on chip." In CLEO: Fundamental Science. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/cleo_fs.2023.ftu4a.1.

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Анотація:
The achievement of quantum-enhanced estimation precision is a fundamental challenge. Here, we prove experimentally the saturation of the ultimate quantum limits employing an integrated photonic sensor and developing a fully artificial intelligence-based adaptive protocol.
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Yoshida, Toshiyuki, and Yoshinori Sakai. "Reliability metric for multiparameter motion estimation and its application." In Electronic Imaging '97, edited by Jan Biemond and Edward J. Delp III. SPIE, 1997. http://dx.doi.org/10.1117/12.263269.

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Cimini, Valeria, Federico Belliardo, Emanuele Polino, Francesco Hoch, Bruno Piccirillo, Nicolò Spagnolo, Vittorio Giovannetti, and Fabio Sciarrino. "Sub-standard quantum limit estimation precision for a wide resources range." In CLEO: Fundamental Science. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/cleo_fs.2023.ftu4a.2.

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Анотація:
Quantum metrology protocols need to face several issues when implemented in realistic scenarios. Here, we experimentally demonstrate quantum-enhanced estiamtion precisions in both a single and multiparameter framework maintained for an exceptionally large range of resources.
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Warsza, Zygmunt Lech, and Jacek Puchalski. "Estimation of Uncertainties in Indirect Multiparameter Measurements of Correlated Quantities." In 2019 12th International Conference on Measurement. IEEE, 2019. http://dx.doi.org/10.23919/measurement47340.2019.8780042.

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Demkowicz-Dobrzanski, Rafal, Francesco Albarelli, and Wojciech Gorecki. "Fundamental Limits in Mutiparameter Quantum Metrology." In Quantum 2.0. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/quantum.2022.qw4c.6.

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Анотація:
We derive fundamental bounds on the maximal achievable precision in multiparameter noisy quantum channel estimation, as well as show operationally meaningful Heisenberg scaling precision limits for the multiple phase interferometry in the absence of noise.
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