Academic literature on the topic 'Variability Models'
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Journal articles on the topic "Variability Models"
Temple, Paul, Mathieu Acher, Jean-Marc Jezequel, and Olivier Barais. "Learning Contextual-Variability Models." IEEE Software 34, no. 6 (November 2017): 64–70. http://dx.doi.org/10.1109/ms.2017.4121211.
Full textLamprecht, Anna-Lena, Stefan Naujokat, and Ina Schaefer. "Variability Management beyond Feature Models." Computer 46, no. 11 (November 2013): 48–54. http://dx.doi.org/10.1109/mc.2013.299.
Full textBeuche, Danilo, Holger Papajewski, and Wolfgang Schröder-Preikschat. "Variability management with feature models." Science of Computer Programming 53, no. 3 (December 2004): 333–52. http://dx.doi.org/10.1016/j.scico.2003.04.005.
Full textRees, Martin J. "Models for Variability in AGNs." Symposium - International Astronomical Union 159 (1994): 239–48. http://dx.doi.org/10.1017/s0074180900175096.
Full textSchipper, M., and C. Wilkinson. "INCORPORATING PRODUCT VARIABILITY INTO QUALITY MODELS." Acta Horticulturae, no. 476 (November 1998): 49–58. http://dx.doi.org/10.17660/actahortic.1998.476.5.
Full textMastichiadis, Apostolos, and John G. Kirk. "Models of Variability in Blazar Jets." Publications of the Astronomical Society of Australia 19, no. 1 (2002): 138–42. http://dx.doi.org/10.1071/as01108.
Full textMerck, Derek, Gregg Tracton, Rohit Saboo, Joshua Levy, Edward Chaney, Stephen Pizer, and Sarang Joshi. "Training models of anatomic shape variability." Medical Physics 35, no. 8 (July 15, 2008): 3584–96. http://dx.doi.org/10.1118/1.2940188.
Full textHayden, Brian. "Resource Models of Inter-Assemblage Variability." Lithic Technology 15, no. 3 (December 1986): 82–89. http://dx.doi.org/10.1080/01977261.1986.11754486.
Full textAslin, Richard N. "MODELS OF OCULOMOTOR VARIABILITY IN INFANCY." Monographs of the Society for Research in Child Development 62, no. 2 (April 1997): 146–49. http://dx.doi.org/10.1111/j.1540-5834.1997.tb00521.x.
Full textvan Groenendaal, Willem J. H. "Estimating NPV variability for deterministic models." European Journal of Operational Research 107, no. 1 (May 1998): 202–13. http://dx.doi.org/10.1016/s0377-2217(97)00138-0.
Full textDissertations / Theses on the topic "Variability Models"
Ternité, Thomas [Verfasser]. "Variability of Development Models / Thomas Ternité." München : Verlag Dr. Hut, 2010. http://d-nb.info/1009972332/34.
Full textScutari, Marco. "Measures of Variability for Graphical Models." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3422736.
Full textNegli ultimi anni i modelli grafici, ed in particolare i network Bayesiani, sono entrati nella pratica corrente delle analisi statistiche in diversi settori scientifici, tra cui medi cina e biostatistica. L’uso di questo tipo di modelli è stato reso possibile dalla rapida evoluzione degli algoritmi per apprenderne la struttura, sia quelli basati su test statistici che quelli basati su funzioni punteggio. L’obiettivo principale di questi nuovi algoritmi è la riduzione del numero di modelli intermedi considerati nell’apprendimento; le loro caratteristiche sono state usualmente valutate usando dei dati di riferimento (per i quali la vera struttura del modello è nota da letteratura) e la distanza di Hamming. Questo approccio tuttavia non può essere usato per dati sperimentali, poiché la loro struttura probabilistica non è nota a priori. In questo caso una valida alternativa è costituita dal bootstrap non parametrico: apprendendo un numero sufficientemente grande di modelli da campioni bootstrap è infatti possibile ottenere una stima empirica della probabilità di ogni caratteristica di interesse del network stesso. In questa tesi viene affrontato il principale limite di questo secondo approccio: la difficoltà di stabilire una soglia di significatività per le probabilità empiriche. Una possibile soluzione è data dall’assunzione di una distribuzione Trinomiale multivariata (nel caso di grafi orientati aciclici) o Bernoulliana multivariata (nel caso di grafi non orientati), che permette di associare ogni arco del network ad una distribuzione mar ginale. Questa assunzione permette di costruire dei test statistici, sia asintotici che esatti, per la variabilità multivariata della struttura del network nel suo complesso o di una sua parte. Tali misure di variabilità sono state poi applicate ad alcuni algoritmi di apprendimento della struttura di network Bayesiani utilizzando il pacchetto R bnlearn, implementato e mantenuto dall’autore.
Arzounian, Dorothée. "Sensory variability and brain state : models, psychophysics, electrophysiology." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCB055/document.
Full textThe same sensory input does not always trigger the same reaction. In laboratory experiments, a given stimulus may elicit a different response on each trial, particularly near the sensory threshold. This is usually attributed to an unspecific source of noise that affects the sensory representation of the stimulus or the decision process. In this thesis we explore the hypothesis that response variability can in part be attributed to measurable, spontaneous fluctuations of ongoing brain state. For this purpose, we develop and test two sets of tools. One is a set of models and psychophysical methods to follow variations of perceptual performance with good temporal resolution and accuracy on different time scales. These methods rely on the adaptive procedures that were developed for the efficient measurements of static sensory thresholds and are extended here for the purpose of tracking time-varying thresholds. The second set of tools we develop encompass data analysis methods to extract from electroencephalography (EEG) signals a quantity that is predictive of behavioral performance on various time scales. We applied these tools to joint recordings of EEG and behavioral data acquired while normal listeners performed a frequency-discrimination task on near-threshold auditory stimuli. Unlike what was reported in the literature for visual stimuli, we did not find evidence for any effects of ongoing low-frequency EEG oscillations on auditory performance. However, we found that a substantial part of judgment variability can be accounted for by effects of recent stimulus-response history on an ongoing decision
Byrne, Nicholas. "Deterministic models of Southern Hemisphere circulation variability." Thesis, University of Reading, 2017. http://centaur.reading.ac.uk/74253/.
Full textStrounine, Kirill. "Reduced models of extratropical low-frequency variability." Diss., Restricted to subscribing institutions, 2007. http://proquest.umi.com/pqdweb?did=1320974401&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.
Full textSchenzinger, Verena. "Tropical stratosphere variability and extratropical teleconnections." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:7f03dad9-8ef6-4586-8caa-314d9c3a15da.
Full textWengel, Christian [Verfasser]. "Equatorial Pacific Variability in Climate Models / Christian Wengel." Kiel : Universitätsbibliothek Kiel, 2018. http://d-nb.info/1160235406/34.
Full textBurrow, Jennifer. "Mechanistic models of recruitment variability in fish populations." Thesis, University of York, 2011. http://etheses.whiterose.ac.uk/1611/.
Full textMANFREDI, PAOLO. "High-Speed Interconnect Models with Stochastic Parameter Variability." Doctoral thesis, Politecnico di Torino, 2013. http://hdl.handle.net/11583/2513763.
Full textDenis, Yvan. "Implémentation de PCM (Process Compact Models) pour l’étude et l’amélioration de la variabilité des technologies CMOS FDSOI avancées." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAT045/document.
Full textRecently, the race for miniaturization has seen its growth slow because of technological challenges it entails. These barriers include the increasing impact of the local variability and processes from the increasing complexity of the manufacturing process and miniaturization, in addition to the difficult of reducing the channel length. To address these challenges, new architectures, very different from the traditional one (bulk), have been proposed. However these new architectures require more effort to be industrialized. Increasing complexity and development time require larger financial investments. In fact there is a real need to improve the development and optimization of devices. This work gives some tips in order to achieve these goals. The idea to address the problem is to reduce the number of trials required to find the optimal manufacturing process. The optimal process is one that results in a device whose performance and dispersion reach the predefined aims. The idea developed in this thesis is to combine TCAD tool and compact models in order to build and calibrate what is called PCM (Process Compact Model). PCM is an analytical model that establishes linkages between process and electrical parameters of the MOSFET. It takes both the benefits of TCAD (since it connects directly to the process parameters electrical parameters) and compact (since the model is analytic and therefore faster to calculate). A sufficiently robust predictive and PCM can be used to optimize performance and overall variability of the transistor through an appropriate optimization algorithm. This approach is different from traditional development methods that rely heavily on scientific expertise and successive tests in order to improve the system. Indeed this approach provides a deterministic and robust mathematical framework to the problem. The concept was developed, tested and applied to transistors 28 and 14 nm FD-SOI and to TCAD simulations. The results are presented and recommendations to implement it at industrial scale are provided. Some perspectives and applications are likewise suggested
Books on the topic "Variability Models"
Mueller, Uli. Testing models of low-frequency variability. Cambridge, Mass: National Bureau of Economic Research, 2006.
Find full textD, Schertzer, ed. Nonlinear variability in geophysics 3. Singapore: World Scientific, 1996.
Find full textJack, King, Milligan Michael, Utility Wind Integration Group. Fall Technical Workshop, and National Renewable Energy Laboratory (U.S.), eds. Allocating variability and reserve requirements. Golden, Colo.]: National Renewable Energy Laboratory, 2011.
Find full textLowry, Michelle. The variability of IPO initial returns. Cambridge, Mass: National Bureau of Economic Research, 2006.
Find full textLowry, Michelle. The variability of ipo initial returns. Cambridge, MA: National Bureau of Economic Research, 2006.
Find full textHodrick, Robert J. The variability of velocity in cash-in-advance models. Cambridge, MA: National Bureau of Economic Research, 1989.
Find full textA, Hicks M., and Institution of Civil Engineers (Great Britain), eds. Risk and variability in geotechnical engineering. London: Thomas Telford, 2007.
Find full textSvensson, Lars E. O. Target zones and interest rate variability. Cambridge, MA: National Bureau of Economic Research, 1989.
Find full textUnited States. National Oceanic and Atmospheric Administration, University Corporation for Atmospheric Research, Atlantic Climate Change Program (U.S.), and Meeting on Atlantic Climate Variability (1997 : Lamont-Doherty Earth Observatory of Columbia University), eds. Proceedings from a Meeting on Atlantic Climate Variability: Meeting on Atlantic Climate Variability. Boulder, Colo.]: [University Corp. for Atmospheric Research], 1997.
Find full textPersson, Torsten. Exchange rate variability and asset trade. Cambridge, MA: National Bureau of Economic Research, 1989.
Find full textBook chapters on the topic "Variability Models"
Haugen, Øystein. "VARY – Variability for You." In Models in Software Engineering, 48–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29645-1_7.
Full textFrankignoul, Claude. "Climate Spectra and Stochastic Climate Models." In Analysis of Climate Variability, 29–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03744-7_3.
Full textSarkisyan, Artem S., and Jürgen E. Sündermann. "Synthesis of Models and Observed Data." In Modelling Ocean Climate Variability, 103–51. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-9208-4_4.
Full textFrankignoul, Claude. "Climate Spectra and Stochastic Climate Models." In Analysis of Climate Variability, 29–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03167-4_3.
Full textKelly, Dana, and Curtis Smith. "Hierarchical Bayes Models for Variability." In Springer Series in Reliability Engineering, 67–88. London: Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-187-5_7.
Full textRees, Martin J. "Models for Variability in AGNs." In Multi-Wavelength Continuum Emission of AGN, 239–48. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-010-9537-2_34.
Full textAvrett, Eugene H. "Modeling Solar Variability—Synthetic Models." In Solar Electromagnetic Radiation Study for Solar Cycle 22, 449–69. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5000-2_40.
Full textDauenhauer, Gerd, Thomas Aschauer, and Wolfgang Pree. "Variability in Automation System Models." In Formal Foundations of Reuse and Domain Engineering, 116–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04211-9_12.
Full textFarhat, Salman, Simon Bliudze, Laurence Duchien, and Olga Kouchnarenko. "Composing Run-Time Variability Models." In Lecture Notes in Computer Science, 234–52. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-77382-2_14.
Full textJensen, O. G., J. P. Todoeschuck, D. J. Crossley, and M. Gregotski. "Fractal Linear Models of Geophysical Processes." In Non-Linear Variability in Geophysics, 227–39. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-009-2147-4_16.
Full textConference papers on the topic "Variability Models"
Strüber, Daniel, Anthony Anjorin, and Thorsten Berger. "Variability representations in class models." In MODELS '20: ACM/IEEE 23rd International Conference on Model Driven Engineering Languages and Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3365438.3410935.
Full textLaguna, Miguel A., and Bruno Gonzalez-Baixauli. "Requirements variability models." In the 2005 symposia. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1234324.1234333.
Full textMærsk-Møller, Hans Martin, and Bo Nørregaard Jørgensen. "Cardinality-dependent variability in orthogonal variability models." In the Sixth International Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2110147.2110166.
Full textEpp, Jordan, Thomas Robert, Olivier Ruch, and Alison Olechowski. "Towards SysML v2 as a Variability Modeling Language." In 2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C). IEEE, 2023. http://dx.doi.org/10.1109/models-c59198.2023.00054.
Full textFilho, João Bosco Ferreira, Olivier Barais, Jérôme Le Noir, and Jean-Marc Jézéquel. "Customizing the common variability language semantics for your domain models." In the VARiability for You Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2425415.2425417.
Full textFeichtinger, Kevin, and Rick Rabiser. "Towards Transforming Variability Models." In SPLC '20: 24th ACM International Systems and Software Product Line Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3382026.3425768.
Full textCombemale, Benoit, Olivier Barais, Omar Alam, and Jörg Kienzle. "Using CVL to operationalize product line development with reusable aspect models." In the VARiability for You Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2425415.2425418.
Full textEyal-Salman, Hamzeh, Abdelhak-Djamel Seriai, Christophe Dony, and Ra'fat Al-msie'deen. "Recovering traceability links between feature models and source code of product variants." In the VARiability for You Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2425415.2425420.
Full textBeuche, Danilo. "Managing variability with feature models." In SPLC '15: 2015 International Conference on Software Product Lines. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2791060.2791113.
Full textBeuche, Danilo, and Michael Schulze. "Managing variability with feature models." In SPLC '14: 18th International Software Product Line Conference. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2648511.2648561.
Full textReports on the topic "Variability Models"
Mueller, Ulrich, and Mark Watson. Testing Models of Low-Frequency Variability. Cambridge, MA: National Bureau of Economic Research, November 2006. http://dx.doi.org/10.3386/w12671.
Full textSperber, K., and H. Annamalai. Asian Summer Monsoon Intraseasonal Variability in General Circulation Models. Office of Scientific and Technical Information (OSTI), February 2004. http://dx.doi.org/10.2172/15009797.
Full textHodrick, Robert, Narayana Kocherlakota, and Deborah Lucas. The Variability of Velocity in Cash-In-Advance Models. Cambridge, MA: National Bureau of Economic Research, March 1989. http://dx.doi.org/10.3386/w2891.
Full textFedorov, Alexey. AMOC decadal variability in Earth system models: Mechanisms and climate impacts. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1378474.
Full textGhil, M., S. Kravtsov, A. W. Robertson, and P. Smyth. Studies of regional-scale climate variability and change. Hidden Markov models and coupled ocean-atmosphere modes. Office of Scientific and Technical Information (OSTI), October 2008. http://dx.doi.org/10.2172/940218.
Full textSperber, K. R. Simulation of the Intraseasonal Variability Over the Eastern Pacific ITCZ in Climate Models. Office of Scientific and Technical Information (OSTI), June 2011. http://dx.doi.org/10.2172/1122207.
Full textEk, M., L. Mahrt, S. Chang, G. Levy, and A. A. Holtslag. Formulation of Subgrid Variability and Boundary-Layer Cloud Cover in Large-Scale Models. Fort Belvoir, VA: Defense Technical Information Center, February 1999. http://dx.doi.org/10.21236/ada360481.
Full textJones, Philip D. Climate data, analysis and models for the study of natural variability and anthropogenic change. Office of Scientific and Technical Information (OSTI), July 2014. http://dx.doi.org/10.2172/1148878.
Full textBrzezinska, Ida, and Paul Jasper. Temperature variability as a driver of poverty in low- and middle-income countries. Data and Evidence to End Extreme Poverty, October 2023. http://dx.doi.org/10.55158/deepwp16.
Full textEngel, Charles, and Kenneth West. Accounting for Exchange Rate Variability in Present-Value Models When the Discount Factor is Near One. Cambridge, MA: National Bureau of Economic Research, February 2004. http://dx.doi.org/10.3386/w10267.
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