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Gotowa bibliografia na temat „Variability Models”

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Data publikacji: 14 grudnia 2024

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Artykuły w czasopismach na temat "Variability Models"

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Temple, Paul, Mathieu Acher, Jean-Marc Jezequel i Olivier Barais. "Learning Contextual-Variability Models". IEEE Software 34, nr 6 (listopad 2017): 64–70. http://dx.doi.org/10.1109/ms.2017.4121211.

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Lamprecht, Anna-Lena, Stefan Naujokat i Ina Schaefer. "Variability Management beyond Feature Models". Computer 46, nr 11 (listopad 2013): 48–54. http://dx.doi.org/10.1109/mc.2013.299.

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Beuche, Danilo, Holger Papajewski i Wolfgang Schröder-Preikschat. "Variability management with feature models". Science of Computer Programming 53, nr 3 (grudzień 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., i C. Wilkinson. "INCORPORATING PRODUCT VARIABILITY INTO QUALITY MODELS". Acta Horticulturae, nr 476 (listopad 1998): 49–58. http://dx.doi.org/10.17660/actahortic.1998.476.5.

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Mastichiadis, Apostolos, i John G. Kirk. "Models of Variability in Blazar Jets". Publications of the Astronomical Society of Australia 19, nr 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, Joshua Levy, Edward Chaney, Stephen Pizer i Sarang Joshi. "Training models of anatomic shape variability". Medical Physics 35, nr 8 (15.07.2008): 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, nr 3 (grudzień 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, nr 2 (kwiecień 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, nr 1 (maj 1998): 202–13. http://dx.doi.org/10.1016/s0377-2217(97)00138-0.

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Rozprawy doktorskie na temat "Variability Models"

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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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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 data sets as benchmarks, and differences from the true structure heve been measured with purely descriptive measures such as Hamming distance. This approach to model validation is not possible for real world data sets, as the true structure of their probability distribution is not known. An alternative is provided by the use of either parametric or nonparametric bootstrap. By applying a learning algorithm to a sufficiently large number of bootstrap samples it is possible to obtain the empirical probability of any feature of the resulting network, such as the structure of the Markov Blanket of a particular node. The fundamental limit in the interpretation of the results is that the “reasonable” level of confidence for thresholding depends on the data and the learning algorithm. In this thesis we extend the aforementioned bootstrap-based approach for the in- ference on the structure of a Bayesian or Markov network. The graph representing the network structure and its underlying undirected graph (in the case of Bayesian networks) are modelled using a multivariate extension of the Trinomial and Bernoulli distributions; each component is associated with an arc. These assumptions allow the derivation of exact and asymptotic measures of the variability of the network structure or any of its parts. These measures are then applied to some common learning strate- gies used in literature using the implementation provided by the bnlearn R package implemented and maintained by the author.
Negli 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.

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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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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 développons et évaluons deux ensembles d'outils. L’un est un ensemble de modèles et de méthodes psychophysiques permettant de suivre les variations de la performance perceptive avec une bonne résolution temporelle et avec précision, sur différentes échelles de temps. Ces méthodes s’appuient sur des procédures adaptatives initialement développées pour mesurer efficacement les seuils de perception statiques et sont étendues ici dans le but de suivre des seuils qui varient au cours du temps. Le deuxième ensemble d'outils que nous développons comprend des méthodes d'analyse de données pour extraire de signaux d’électroencéphalographie (EEG) une quantité prédictive de la performance comportementale à diverses échelles de temps. Nous avons appliqué ces outils à des enregistrements conjoints d’EEG et de données comportementales collectées pendant que des auditeurs normo-entendants réalisaient une tâche de discrimination de fréquence sur des stimuli auditifs proche du seuil de discrimination. Contrairement à ce qui a été rapporté dans la littérature concernant des stimuli visuels, nous n'avons pas trouvé de preuve d’un quelconque effet des oscillations EEG spontanées de basse fréquence sur la performance auditive. En revanche, nous avons trouvé qu'une part importante de la variabilité des jugements peut s’expliquer par des effets de l'historique récent des stimuli et des réponses sur la décision prise à un moment donné
The 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

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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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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 solutions, the identification of a seasonal cycle with a long-term average may not be the optimal procedure. Previous research has suggested that multiple evolutions of the seasonal cycle of the Southern Hemisphere mid-latitude circulation may be possible. The central goal of this thesis is to build on this work and to present evidence for different seasonal evolutions of the Southern Hemisphere mid-latitude circulation. This evidence is initially presented by highlighting a low-frequency peak in an aspect of the Southern Hemisphere mid-latitude circulation that is viewed as a harmonic of the annual cycle (quasi-two year). Statistically stationary models of variability about a long-term average are argued to be unable to account for the presence of this harmonic. Following this, an alternative model of circulation variability is proposed that explicitly references various stages of the seasonal cycle in a deterministic manner. In particular, explicit reference is made to the downward shift and to the final breakdown of the stratospheric polar vortex. A re-interpretation of several previous results in the literature including Southern Annular Mode persistence timescales, Southern Hemisphere mid-latitude climate change and the semi-annual oscillation of the mid-latitude jet is subsequently presented using this alternative perspective.

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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 period, knowing its influences on other parts of the climate system can be used to improve long range forecasts. These teleconnections of the QBO in observations are investigated using composite analysis, multilinear regression and a novel approach called causal effect networks (CEN). Findings from these analyses confirm previous results of the QBO modulating the stratospheric polar vortex and subsequently the North Atlantic Oscillation (NAO). They also suggest that it is important to take the equatorial zonal mean zonal wind vertical profile into account when studying teleconnections, rather than the more traditional method of using just one single level. While QBO influences on the Northern Hemisphere winter polar vortex and the NAO are more clearly established, interactions within the tropics remain inconclusive. Regression analysis does not show a connection between the QBO and the MJO, whereas the CEN analysis does. Further studies are needed to understand the interaction mechanisms near the equator. Finally, following the unprecedented disruption of the QBO cycle in the winter 2015/16, the event is described and a model analogue from the MPI-ESM-MR historical simulation is presented. Future implications are unclear, although model projections indicate more frequent QBO irregularities in a warming climate.

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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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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 number surviving to join the adult population is highly variable. This thesis presents simple stochastic, mechanistic larval growth models, developed and utilised to investigate recruitment probabilities and variability. The models are mechanistic in that they are based on consideration of the key ecological processes at work, and not on statistical regression analyses or similar techniques. At the heart of the thesis lies a stochastic drift-diffusion model for the growth of an individual larva. Further mathematical and ecological complexity is built up through consideration of both the temporal and spatial heterogeneity of larval food sources, primarily zooplankton. Results illustrate the impact of stochasticity in the timing of peak food abundance, and the patchiness of the prey, on recruitment variability. The idea of non-constant variance in recruitment is also investigated, with the aim of testing its practical relevance to fisheries management. It is demonstrated that the currently available stock-recruitment time series are at least one order of magnitude too short to reliably fit such models. Management implications are illustrated using simple models and published recruitment data for two exploited stocks. The work developed within this thesis highlights the importance of stochasticity in fish larval growth and recruitment, and the power of simple mechanistic models in examining these ideas.

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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 as long as we are able to assign accurate values to each system parameter. In modern high-speed and high-density designs, process variations and uncertainties in operating conditions result in parameters which are hard to control or partially unavailable. The device response is thus no longer regarded as deterministic, but is more suitably interpreted as a random process. In this framework, the assessment of signal integrity requires a statistical analysis, which is traditionally based on the so-called Monte Carlo or other sampling-based methods. Yet, for practical applications, these approaches are often too time-consuming, as they are known to require a large number of samples to converge. In this thesis, we extend available literature results to the efficient analysis of high-speed interconnects, such as avionic and industrial cables or printed circuit board traces, affected by uncertainties, like process variations or unavailable operating conditions. Specifically, the framework of polynomial chaos theory is adopted to create stochastic models for transmission lines which are faster to be simulated compared to repeated Monte Carlo simulations. Such methodology is based on the expansion of random quantities in series of orthogonal polynomials, and has been already and successfully applied to the analysis of lumped circuits. In this work, the modeling of distributed components, which are key elements for modern high-frequency designs, is addressed. The advocated approach is general and overcomes the limitations of available literature models for the statistical analysis of the signal propagation over interconnects, which are based on simplified structures and approximate assumptions. Also, a SPICE-compatible implementation is presented, thus allowing the convenient use of SPICE-like circuit analysis tools for the simulation of complex stochastic network topologies, avoiding the creation of customized, ad hoc implementations. This thesis provides a comprehensive theoretical discussion together with several tutorial application examples, thus complementing the published material.

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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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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 et du temps de développement requièrent de plus gros investissements financier. De fait il existe un besoin réel d’améliorer le développement et l’optimisation des dispositifs. Ce travail donne quelques pistes dans le but d’atteindre ces objectifs. L’idée, pour répondre au problème, est de réduire le nombre d’essai nécessaire pour trouver le processus de fabrication optimal. Le processus optimal est celui qui conduit à un dispositif dont les performances et leur dispersion atteignent les objectifs prédéfinis. L’idée développée dans cette thèse est de combiner l’outil TCAD et les modèles compacts dans le but de construire et calibrer ce que l’on appelle un PCM (Process Compact Model). Un PCM est un modèle analytique qui établit les liens entre les paramètres process et électriques du MOSFET. Il tire à la fois les bénéfices de la TCAD (puisqu’il relie directement les paramètres process aux paramètres électriques) et du modèle compact (puisque le modèle est analytique et donc rapide à calculer). Un PCM suffisamment prédictif et robuste peut être utilisé pour optimiser les performances et la variabilité globale du transistor grâce à un algorithme d’optimisation approprié. Cette approche est différente des méthodes de développement classiques qui font largement appel à l’expertise scientifique et à des essais successifs dans le but d’améliorer le dispositif. En effet cette approche apporte un cadre mathématique déterministe et robuste au problème.Le concept a été développé, testé et appliqué aux transistors 28 et 14 nm FD-SOI ainsi qu’aux simulations TCAD. Les résultats sont exposés ainsi que les recommandations nécessaires pour implémenter la technique à échelle industrielle. Certaines perspectives et applications sont de même suggérées
Recently, 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

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Książki na temat "Variability Models"

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

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D, Schertzer, red. Nonlinear variability in geophysics 3. Singapore: World Scientific, 1996.

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Jack, King, Milligan Michael, Utility Wind Integration Group. Fall Technical Workshop i National Renewable Energy Laboratory (U.S.), red. Allocating variability and reserve requirements. Golden, Colo.]: National Renewable Energy Laboratory, 2011.

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

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

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

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

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

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United States. National Oceanic and Atmospheric Administration, University Corporation for Atmospheric Research, Atlantic Climate Change Program (U.S.) i Meeting on Atlantic Climate Variability (1997 : Lamont-Doherty Earth Observatory of Columbia University), red. Proceedings from a Meeting on Atlantic Climate Variability: Meeting on Atlantic Climate Variability. Boulder, Colo.]: [University Corp. for Atmospheric Research], 1997.

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

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Części książek na temat "Variability Models"

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Haugen, Øystein. "VARY – Variability for You". W Models in Software Engineering, 48–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29645-1_7.

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Frankignoul, Claude. "Climate Spectra and Stochastic Climate Models". W Analysis of Climate Variability, 29–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-662-03744-7_3.

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Sarkisyan, Artem S., i Jürgen E. Sündermann. "Synthesis of Models and Observed Data". W Modelling Ocean Climate Variability, 103–51. Dordrecht: 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". W Analysis of Climate Variability, 29–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-662-03167-4_3.

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Kelly, Dana, i Curtis Smith. "Hierarchical Bayes Models for Variability". W Springer Series in Reliability Engineering, 67–88. London: 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". W Multi-Wavelength Continuum Emission of AGN, 239–48. Dordrecht: 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". W 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.

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Dauenhauer, Gerd, Thomas Aschauer i Wolfgang Pree. "Variability in Automation System Models". W 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.

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Farhat, Salman, Simon Bliudze, Laurence Duchien i Olga Kouchnarenko. "Composing Run-Time Variability Models". W Lecture Notes in Computer Science, 234–52. Cham: 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 i M. Gregotski. "Fractal Linear Models of Geophysical Processes". W Non-Linear Variability in Geophysics, 227–39. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-009-2147-4_16.

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Streszczenia konferencji na temat "Variability Models"

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Strüber, Daniel, Anthony Anjorin i Thorsten Berger. "Variability representations in class models". W 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.

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Laguna, Miguel A., i Bruno Gonzalez-Baixauli. "Requirements variability models". W the 2005 symposia. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1234324.1234333.

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

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Epp, Jordan, Thomas Robert, Olivier Ruch i Alison Olechowski. "Towards SysML v2 as a Variability Modeling Language". W 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 i Jean-Marc Jézéquel. "Customizing the common variability language semantics for your domain models". W the VARiability for You Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2425415.2425417.

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Feichtinger, Kevin, i Rick Rabiser. "Towards Transforming Variability Models". W 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.

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Combemale, Benoit, Olivier Barais, Omar Alam i Jörg Kienzle. "Using CVL to operationalize product line development with reusable aspect models". W the VARiability for You Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2425415.2425418.

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Eyal-Salman, Hamzeh, Abdelhak-Djamel Seriai, Christophe Dony i Ra'fat Al-msie'deen. "Recovering traceability links between feature models and source code of product variants". W the VARiability for You Workshop. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2425415.2425420.

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

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

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Raporty organizacyjne na temat "Variability Models"

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

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

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Hodrick, Robert, Narayana Kocherlakota i Deborah Lucas. The Variability of Velocity in Cash-In-Advance Models. Cambridge, MA: National Bureau of Economic Research, marzec 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), wrzesień 2017. http://dx.doi.org/10.2172/1378474.

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Ghil, M., S. Kravtsov, A. W. Robertson i 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), październik 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), czerwiec 2011. http://dx.doi.org/10.2172/1122207.

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Ek, M., L. Mahrt, S. Chang, G. Levy i A. A. Holtslag. Formulation of Subgrid Variability and Boundary-Layer Cloud Cover in Large-Scale Models. Fort Belvoir, VA: Defense Technical Information Center, luty 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), lipiec 2014. http://dx.doi.org/10.2172/1148878.

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

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Streszczenie:

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, i 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, luty 2004. http://dx.doi.org/10.3386/w10267.

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