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Books on the topic 'Predicting filter'

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Author: Grafiati
Published: 30 May 2022
Last updated: 31 May 2022

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1

Luhmann, Hans-Jochen. Die Blindheit der Gesellschaft: Filter der Risikowahrnehmung. München: Gerling Akademie Verlag, 2001.

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2

Jones, J. C. Peyton. Testing for nonlinearity using a prediction error filter. Sheffield: University of Sheffield, Dept. of Control Engineering, 1988.

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3

Mutaf, Asim. A Kalman filter with smoothing for hurricane tracking and prediction. Monterey, Calif: Naval Postgraduate School, 1989.

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4

Nonlinear prediction ladder-filters for higher-order stochastic sequences. Berlin: Springer-Verlag, 1985.

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5

Zarzycki, Jan, ed. Nonlinear Prediction Ladder-Filters for Higher-Order Stochastic Sequences. Berlin/Heidelberg: Springer-Verlag, 1985. http://dx.doi.org/10.1007/bfb0007210.

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6

Kiruluta, Andrew M. Predictive head movement tracking using innovations generated by Kalman filters. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1993.

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7

1952-, Sin Kwai Sang, ed. Adaptive filtering prediction and control. Mineola, N.Y: Dover, 2009.

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8

Chuah, Teong Guan. Prediction of the pressure and velocity distributions in rigid ceramic filters. Birmingham: University of Birmingham, 2001.

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9

Gaver, Donald Paul. A Kalman filter for a Poisson series with covariates and Laplace approximation integration. Monterey, Calif: Naval Postgraduate School, 1991.

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10

Levan, N. On-line range prediction system (II). [Los Angeles, Calif: UCLA School of Engineering and Applied Science, 1988.

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11

Levan, N. On-line range prediction system (II). [Los Angeles, Calif: UCLA School of Engineering and Applied Science, 1988.

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12

An introduction to stochastic filtering theory. Oxford: Oxford University Press, 2008.

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13

Predictive analytics: Microsoft Excel. Indianapolis, Ind: Que Pub., 2013.

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14

Implicit filtering. Philadelphia: Society for Industrial and Applied Mathematics, 2011.

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15

Sarma, Kattamuri S. Predictive modeling with SAS Enterprise Miner: Practical solutions for business applications. Cary, NC: SAS Institute, 2007.

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16

translator, Cheng Yongjia, ed. Shi yong EXCEL xue xi tong ji yu ce fa: Excel de manabu tōkeiteki yosoku = Statistical prediction. Taibei Shi: Tai dian dian ye gu fen you xian gong si, 2016.

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17

M, Rogers Robert. Final report for space shuttle propulsion estimation development verification: Volume I / by Robert M. Rogers. [Washington, DC: National Aeronautics and Space Administration, 1989.

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18

M, Rogers Robert. Final report for space shuttle propulsion estimation development verification: Volume II. [Washington, DC: National Aeronautics and Space Administration, 1989.

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19

Monnie, McGee, ed. Introduction to time series analysis and forecasting: With applications in SAS and SPSS. San Diego: Academic Press, 2000.

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20

A Discrete Digital Filter for Forward Prediction of Seaway Elevation Response. Storming Media, 1997.

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21

Nonlinear Prediction Ladder-Filters for Higher-Order Stochastic Sequences. Springer, 1985.

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22

S, Pototzky Anthony, Perry Boyd, and Langley Research Center, eds. Further studies using matched filter theory and stochastic simulation for gust loads prediction. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1993.

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23

Sobiski, Donald John. Predictive compensation of visual system time delays. 1988.

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24

Filtering and Prediction: A Primer (Student Mathematical Library). American Mathematical Society, 2007.

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25

1957-, Tischler Mark B., and Ames Research Center, eds. An empirical correction method for improving off-axes response prediction in component type flight mechanics helicopter models. Moffett Field, CA: National Aeronautics and Space Administration, Ames Research Center, 1997.

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26

Predictive Modeling With SAS Enterprise Miner: Practical Solutions for Business Applications. SAS Publishing, 2007.

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27

Facility, Dryden Flight Research, ed. A simulation study of turbofan engine deterioration estimation using Kalman filtering techniques. Edwards, Calif: National Aeronautics and Space Administration, Ames Research Center, Dryden Flight Research Facility, 1991.

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28

Wan, Wilfred, and Etel Solingen. International Security: Nuclear Proliferation. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228637.013.121.

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Abstract:
Since the advent of the nuclear age, scholars have sought to provide rationales behind decisions to pursue, forgo, or relinquish nuclear weapons programs. Security, status, cost, technical capabilities, and domestic considerations have played central roles in explaining those choices. Classical neorealism was once the conventional wisdom, advancing that relative power and the logic of self-help in an anarchic world drove states to nuclear weapons. Yet, the analysis of nuclear proliferation has evolved in accordance with broader debates in international relations theory in recent decades, including the incorporation of neoliberal institutionalist, constructivist, and domestic political perspectives. The end of the Cold War and the upheaval of international order in particular marked a watershed for the literature, with scholars challenging the dominant paradigm by examining the effects of institutions, norms, and identities. Those approaches, however, under-theorized—if not omitted altogether—the role of domestic political drivers in choices to acquire or abstain from acquiring from nuclear weapons. Such drivers provide filters that can be invaluable in explaining whether, when, and how state actors are susceptible to considerations of relative power, international institutions, and norms. More recently, scholars have deployed more sophisticated theoretical frameworks and diverse methodologies. The road ahead requires greater analytical flexibility, harnessing the utility of classical perspectives while adding enough nuance to increase explanatory power, greater attentiveness to the complex interaction among variables, and improved specification and operationalization amenable to rigorous testing, all with an eye toward enhancing both historical accuracy and predictive capabilities.
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29

Predictive Analytics Using Oracle Data Miner: Develop and Use Data Mining Models in ODM, SQL and PL/SQL. McGraw-Hill Education, 2014.

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30

Performance analysis of a GPS interferometric attitude determination system for a gravity gradient stabilized spacecraft. Cambridge, Mass: Charles Stark Draper Laboratory, 1995.

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31

United States. National Aeronautics and Space Administration., ed. Performance analysis of a GPS interferometric attitude determination system for a gravity gradient stabilized spacecraft. Cambridge, Mass: Charles Stark Draper Laboratory, 1995.

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32

United States. National Aeronautics and Space Administration., ed. Performance analysis of a GPS interferometric attitude determination system for a gravity gradient stabilized spacecraft. Cambridge, Mass: Charles Stark Draper Laboratory, 1995.

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