Bibliografie tematiche / Variability Models

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Letteratura scientifica selezionata sul tema "Variability Models"

Autore: Grafiati
Pubblicato: 14 dicembre 2024
Ultima modifica: 25 luglio 2025

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Articoli di riviste sul tema "Variability Models"

1

Temple, Paul, Mathieu Acher, Jean-Marc Jezequel, and Olivier Barais. "Learning Contextual-Variability Models." IEEE Software 34, no. 6 (2017): 64–70. http://dx.doi.org/10.1109/ms.2017.4121211.

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Lamprecht, Anna-Lena, Stefan Naujokat, and Ina Schaefer. "Variability Management beyond Feature Models." Computer 46, no. 11 (2013): 48–54. http://dx.doi.org/10.1109/mc.2013.299.

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Beuche, Danilo, Holger Papajewski, and Wolfgang Schröder-Preikschat. "Variability management with feature models." Science of Computer Programming 53, no. 3 (2004): 333–52. http://dx.doi.org/10.1016/j.scico.2003.04.005.

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Rees, Martin J. "Models for Variability in AGNs." Symposium - International Astronomical Union 159 (1994): 239–48. http://dx.doi.org/10.1017/s0074180900175096.

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In this talk I shall address three different processes relevant to continuum variability in AGNs. The first two refer to the physical conditions in the regions responsible for the non-thermal emission, and the implications of high brightness temperatures. The third is the distinctive type of flare that results when a star is tidally disrupted by a massive black hole; this process, which merits much further study, it likely to be specially important as a diagnostic of physical conditions in low-luminosity nearby nuclei.
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Schipper, 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.

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Mastichiadis, 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.

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AbstractDuring the last decade multiwavelength observations of blazars have revealed many interesting patterns in their emission across the EM spectrum. In the present article we will review the time-dependent one-zone models and the models which advocate an acceleration and a radiation zone, and we will make some comparisons between them, especially in light of recent observations of the so-called TeV blazars.
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Merck, Derek, Gregg Tracton, Rohit Saboo, et al. "Training models of anatomic shape variability." Medical Physics 35, no. 8 (2008): 3584–96. http://dx.doi.org/10.1118/1.2940188.

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Hayden, Brian. "Resource Models of Inter-Assemblage Variability." Lithic Technology 15, no. 3 (1986): 82–89. http://dx.doi.org/10.1080/01977261.1986.11754486.

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Aslin, Richard N. "MODELS OF OCULOMOTOR VARIABILITY IN INFANCY." Monographs of the Society for Research in Child Development 62, no. 2 (1997): 146–49. http://dx.doi.org/10.1111/j.1540-5834.1997.tb00521.x.

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van Groenendaal, Willem J. H. "Estimating NPV variability for deterministic models." European Journal of Operational Research 107, no. 1 (1998): 202–13. http://dx.doi.org/10.1016/s0377-2217(97)00138-0.

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Tesi sul tema "Variability Models"

1

Ternité, Thomas [Verfasser]. "Variability of Development Models / Thomas Ternité." München : Verlag Dr. Hut, 2010. http://d-nb.info/1009972332/34.

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Scutari, Marco. "Measures of Variability for Graphical Models." Doctoral thesis, Università degli studi di Padova, 2011. http://hdl.handle.net/11577/3422736.

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Abstract (sommario):
In recent years, graphical models have been successfully applied in several different disciplines, including medicine, biology and epidemiology. This has been made possible by the rapid evolution of structure learning algorithms, from constraint-based ones to score-based and hybrid ones. The main goal in the development of these algorithms has been the reduction of the number of either independence tests or score comparisons needed to learn the structure of the Bayesian network. In most cases the characteristics of the learned networks have been studied using a small number of reference
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Arzounian, Dorothée. "Sensory variability and brain state : models, psychophysics, electrophysiology." Thesis, Sorbonne Paris Cité, 2017. http://www.theses.fr/2017USPCB055/document.

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Abstract (sommario):
La même entrée sensorielle ne provoque pas toujours la même réaction. Dans les expériences en laboratoire, un stimulus donné peut engendrer une réponse différente à chaque nouvel essai, en particulier à proximité du seuil sensoriel. Ce phénomène est généralement attribué à une source de bruit non spécifique qui affecte la représentation sensorielle du stimulus ou le processus décisionnel. Dans cette thèse, nous examinons l'hypothèse selon laquelle cette variabilité des réponses peut être attribuée en partie à des fluctuations mesurables et spontanées de l'état cérébral. Dans ce but, nous dével
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Byrne, Nicholas. "Deterministic models of Southern Hemisphere circulation variability." Thesis, University of Reading, 2017. http://centaur.reading.ac.uk/74253/.

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Abstract (sommario):
Statistical models of atmospheric variability typically attempt to account for deterministic seasonal variations by constructing a long-term average for each day or month of the year. Year-to-year variability can then be treated as some form of stochastic process about this long-term average. In general, the stochastic processes are assumed to be statistically stationary (invariant under time translation). However, for a non-linear system such as the Earth’s atmosphere, multiple seasonal evolutions may be possible for the same external forcing. In the presence of such a multiplicity of solutio
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Strounine, 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.

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Schenzinger, Verena. "Tropical stratosphere variability and extratropical teleconnections." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:7f03dad9-8ef6-4586-8caa-314d9c3a15da.

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The Quasi-Biennial Oscillation (QBO) is the dominant pattern of variability in the tropical stratosphere. Despite a well established theory regarding its generation in the atmosphere, the simulation in global climate models remains difficult. A set of metrics assessing the quality of model simulations is presented in this study. The QBO simulations in models submitted to the CMIP5 and CCMVal-2 intercomparison projects are characterised and compared to radiosonde observations and reanalysis datasets. Common model biases and their potential causes are addressed. As the QBO has a long intrinsic p
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Wengel, Christian [Verfasser]. "Equatorial Pacific Variability in Climate Models / Christian Wengel." Kiel : Universitätsbibliothek Kiel, 2018. http://d-nb.info/1160235406/34.

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Burrow, Jennifer. "Mechanistic models of recruitment variability in fish populations." Thesis, University of York, 2011. http://etheses.whiterose.ac.uk/1611/.

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Abstract (sommario):
There are serious concerns worldwide about the decline of exploited fish stocks. The number of fish larvae surviving to be recruited into the adult population each year is fundamental to the long-term stability of a fish stock. Monitoring and predicting recruitment is a crucial component of managing economically important fisheries worldwide. Fish recruitment can vary by an order of magnitude, or more, between years, and the larval stage is a key determining factor. Fish larvae are born into an extremely variable environment, with high mortality rates, and so it is not surprising that the numb
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MANFREDI, PAOLO. "High-Speed Interconnect Models with Stochastic Parameter Variability." Doctoral thesis, Politecnico di Torino, 2013. http://hdl.handle.net/11583/2513763.

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In the process of design and fabrication of electronic products, numerical simulation plays a fundamental role for a preliminary electromagnetic compatibility (EMC) assessment of devices in the early design phase. Direct EMC measurements impact both cost and time-to-market as they require purchase and/or hiring of facilities and instruments, as well as fabrication of prototype devices, and need therefore to be minimized. Nowadays, designers can rely on several sophisticated modeling tools, helping them to perform right-the-first-time designs. Nonetheless, these simulation models are accurate a
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Denis, 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.

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Abstract (sommario):
Récemment, la course à la miniaturisation a vue sa progression ralentir à cause des défis technologiques qu’elle implique. Parmi ces obstacles, on trouve l’impact croissant de la variabilité local et process émanant de la complexité croissante du processus de fabrication et de la miniaturisation, en plus de la difficulté à réduire la longueur du canal. Afin de relever ces défis, de nouvelles architectures, très différentes de celle traditionnelle (bulk), ont été proposées. Cependant ces nouvelles architectures demandent plus d’efforts pour être industrialisées. L’augmentation de la complexité
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Libri sul tema "Variability Models"

1

Mueller, Uli. Testing models of low-frequency variability. National Bureau of Economic Research, 2006.

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2

D, Schertzer, ed. Nonlinear variability in geophysics 3. World Scientific, 1996.

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3

Jack, King, Milligan Michael, Utility Wind Integration Group. Fall Technical Workshop, and National Renewable Energy Laboratory (U.S.), eds. Allocating variability and reserve requirements. National Renewable Energy Laboratory, 2011.

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4

Lowry, Michelle. The variability of IPO initial returns. National Bureau of Economic Research, 2006.

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Lowry, Michelle. The variability of ipo initial returns. National Bureau of Economic Research, 2006.

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6

Hodrick, Robert J. The variability of velocity in cash-in-advance models. National Bureau of Economic Research, 1989.

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7

A, Hicks M., and Institution of Civil Engineers (Great Britain), eds. Risk and variability in geotechnical engineering. Thomas Telford, 2007.

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8

Svensson, Lars E. O. Target zones and interest rate variability. National Bureau of Economic Research, 1989.

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9

United 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. [University Corp. for Atmospheric Research], 1997.

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10

Persson, Torsten. Exchange rate variability and asset trade. National Bureau of Economic Research, 1989.

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Capitoli di libri sul tema "Variability Models"

1

Haugen, Øystein. "VARY – Variability for You." In Models in Software Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29645-1_7.

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2

Frankignoul, Claude. "Climate Spectra and Stochastic Climate Models." In Analysis of Climate Variability. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03744-7_3.

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Sarkisyan, Artem S., and Jürgen E. Sündermann. "Synthesis of Models and Observed Data." In Modelling Ocean Climate Variability. Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-9208-4_4.

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Frankignoul, Claude. "Climate Spectra and Stochastic Climate Models." In Analysis of Climate Variability. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03167-4_3.

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Kelly, Dana, and Curtis Smith. "Hierarchical Bayes Models for Variability." In Springer Series in Reliability Engineering. Springer London, 2011. http://dx.doi.org/10.1007/978-1-84996-187-5_7.

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Rees, Martin J. "Models for Variability in AGNs." In Multi-Wavelength Continuum Emission of AGN. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-010-9537-2_34.

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Avrett, Eugene H. "Modeling Solar Variability—Synthetic Models." In Solar Electromagnetic Radiation Study for Solar Cycle 22. Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-011-5000-2_40.

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Dauenhauer, Gerd, Thomas Aschauer, and Wolfgang Pree. "Variability in Automation System Models." In Formal Foundations of Reuse and Domain Engineering. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04211-9_12.

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Farhat, Salman, Simon Bliudze, Laurence Duchien, and Olga Kouchnarenko. "Composing Run-Time Variability Models." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-77382-2_14.

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Jensen, O. G., J. P. Todoeschuck, D. J. Crossley, and M. Gregotski. "Fractal Linear Models of Geophysical Processes." In Non-Linear Variability in Geophysics. Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-009-2147-4_16.

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Atti di convegni sul tema "Variability Models"

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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. ACM, 2020. http://dx.doi.org/10.1145/3365438.3410935.

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Laguna, Miguel A., and Bruno Gonzalez-Baixauli. "Requirements variability models." In the 2005 symposia. ACM Press, 2005. http://dx.doi.org/10.1145/1234324.1234333.

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Mærsk-Møller, Hans Martin, and Bo Nørregaard Jørgensen. "Cardinality-dependent variability in orthogonal variability models." In the Sixth International Workshop. ACM Press, 2012. http://dx.doi.org/10.1145/2110147.2110166.

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Epp, 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.

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Filho, 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. ACM Press, 2012. http://dx.doi.org/10.1145/2425415.2425417.

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Feichtinger, Kevin, and Rick Rabiser. "Towards Transforming Variability Models." In SPLC '20: 24th ACM International Systems and Software Product Line Conference. ACM, 2020. http://dx.doi.org/10.1145/3382026.3425768.

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Combemale, 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. ACM Press, 2012. http://dx.doi.org/10.1145/2425415.2425418.

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Eyal-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. ACM Press, 2012. http://dx.doi.org/10.1145/2425415.2425420.

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Beuche, Danilo. "Managing variability with feature models." In SPLC '15: 2015 International Conference on Software Product Lines. ACM, 2015. http://dx.doi.org/10.1145/2791060.2791113.

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Beuche, Danilo, and Michael Schulze. "Managing variability with feature models." In SPLC '14: 18th International Software Product Line Conference. ACM, 2014. http://dx.doi.org/10.1145/2648511.2648561.

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Rapporti di organizzazioni sul tema "Variability Models"

1

Mueller, Ulrich, and Mark Watson. Testing Models of Low-Frequency Variability. National Bureau of Economic Research, 2006. http://dx.doi.org/10.3386/w12671.

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2

Sperber, K., and H. Annamalai. Asian Summer Monsoon Intraseasonal Variability in General Circulation Models. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/15009797.

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Hodrick, Robert, Narayana Kocherlakota, and Deborah Lucas. The Variability of Velocity in Cash-In-Advance Models. National Bureau of Economic Research, 1989. http://dx.doi.org/10.3386/w2891.

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Fedorov, Alexey. AMOC decadal variability in Earth system models: Mechanisms and climate impacts. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1378474.

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Ghil, 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), 2008. http://dx.doi.org/10.2172/940218.

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Sperber, K. R. Simulation of the Intraseasonal Variability Over the Eastern Pacific ITCZ in Climate Models. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1122207.

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Ek, M., L. Mahrt, S. Chang, G. Levy, and A. A. Holtslag. Formulation of Subgrid Variability and Boundary-Layer Cloud Cover in Large-Scale Models. Defense Technical Information Center, 1999. http://dx.doi.org/10.21236/ada360481.

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Jones, Philip D. Climate data, analysis and models for the study of natural variability and anthropogenic change. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1148878.

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Brzezinska, Ida, and Paul Jasper. Temperature variability as a driver of poverty in low- and middle-income countries. Data and Evidence to End Extreme Poverty, 2023. http://dx.doi.org/10.55158/deepwp16.

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Abstract (sommario):
Temperature variability has been shown to reduce macroeconomic growth and to negatively affect household wealth. Climate models predict that LMICs are located in ‘hotspot’ areas that will experience the largest increases in temperature variability. Understanding the effects of an increasingly variable climate on social and economic outcomes is thus of key importance.
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Engel, Charles, and Kenneth West. Accounting for Exchange Rate Variability in Present-Value Models When the Discount Factor is Near One. National Bureau of Economic Research, 2004. http://dx.doi.org/10.3386/w10267.

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