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Journal articles on the topic 'Variability Models'

Author: Grafiati

Published: 14 December 2024

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1

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.

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2

Lamprecht, 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.

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3

Beuche, 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.

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4

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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5

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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6

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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7

Merck, 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.

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8

Hayden, 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.

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9

Aslin, 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.

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10

van 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.

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11

Christensen, O. B., M. A. Gaertner, J. A. Prego, and J. Polcher. "Internal variability of regional climate models." Climate Dynamics 17, no. 11 (August 1, 2001): 875–87. http://dx.doi.org/10.1007/s003820100154.

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12

Srivastava, A. "Stochastic models for capturing image variability." IEEE Signal Processing Magazine 19, no. 5 (September 2002): 63–76. http://dx.doi.org/10.1109/msp.2002.1028353.

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13

Xie, Hongmei, Keshe Ni, and Wenyu Liu. "Variability ordering of multiplicative frailty models." Metrika 79, no. 6 (December 12, 2015): 659–70. http://dx.doi.org/10.1007/s00184-015-0571-7.

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14

Karataş, Ahmet Serkan, and Halit Oğuztüzün. "Attribute-based variability in feature models." Requirements Engineering 21, no. 2 (November 6, 2014): 185–208. http://dx.doi.org/10.1007/s00766-014-0216-9.

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15

Donnelly, Peter, and Ward Whitt. "On reinforcement-depletion compartmental urn models." Journal of Applied Probability 26, no. 3 (September 1989): 477–89. http://dx.doi.org/10.2307/3214406.

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We verify and extend a conjecture of Purdue (1981) concerning the stochastic monotonicity of absorption times in a class of compartmental urn models. We also describe the effect of increased variability in the reinforcement sizes. Finally, we investigate variability in the content process for large populations. In many applications, compartmental models substantially under-represent the variability observed in the data, so that there has been considerable interest in modifying the model to increase the variability. We show that the squared coefficient of variation of the content is not asymptotically negligible when both the size and the variability of the reinforcements are of the same order as the initial population.

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16

Donnelly, Peter, and Ward Whitt. "On reinforcement-depletion compartmental urn models." Journal of Applied Probability 26, no. 03 (September 1989): 477–89. http://dx.doi.org/10.1017/s0021900200038080.

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We verify and extend a conjecture of Purdue (1981) concerning the stochastic monotonicity of absorption times in a class of compartmental urn models. We also describe the effect of increased variability in the reinforcement sizes. Finally, we investigate variability in the content process for large populations. In many applications, compartmental models substantially under-represent the variability observed in the data, so that there has been considerable interest in modifying the model to increase the variability. We show that the squared coefficient of variation of the content is not asymptotically negligible when both the size and the variability of the reinforcements are of the same order as the initial population.

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17

Zhang, L. L., L. M. Zhang, and W. H. Tang. "Similarity of soil variability in centrifuge models." Canadian Geotechnical Journal 45, no. 8 (August 2008): 1118–29. http://dx.doi.org/10.1139/t08-066.

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The soil specimen in a centrifuge model is subject to spatial variability depending on the method of sample preparation and the stress field induced by the centrifugal acceleration, even though it is intended to be uniformly prepared. In contrast to extensive measurements for studying the variability of in situ soil properties, soil variability in centrifuge models, especially that which is based on data at very close sampling distances, is less understood. In this paper, the variability of soil density in two centrifuge models is presented. Random field theory is adopted to characterize the spatial soil variability in the two centrifuge models. The importance of taking spatial variability parameters as a model similarity requirement in centrifuge model design is illustrated and discussed. It is demonstrated that, although centrifuge models of different sizes can be designed to simulate the same prototype, the prototypes these models actually represent are not identical in terms of soil spatial variability. To achieve similarity in spatial variability between a centrifuge model and its prototype, one may need to control either the point coefficient of variation or the scale of fluctuation of the model soil so that the coefficients of variation of the spatially averaged soil property in the model and the prototype are the same.

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18

Sullivan, Roger J., and Henry F. Lyle, III. "Economic models are not evolutionary models." Behavioral and Brain Sciences 28, no. 6 (December 2005): 836. http://dx.doi.org/10.1017/s0140525x05430149.

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Henrich et al. reject the “selfishness axiom” within a narrowly-defined economic model, and are premature in claiming that they have demonstrated cross-cultural variability in “selfishness” as defined in broader evolutionary theory. We also question whether a key experimental condition, anonymity, can be maintained in the small, cohesive, social groupings employed in the study.

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19

Kawaguchi, Toshihiro, and Shin Mineshige. "AGN Variability." Symposium - International Astronomical Union 194 (1999): 356–63. http://dx.doi.org/10.1017/s0074180900162291.

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A number of monitoring observations of continuum emission from Active Galactic Nuclei (AGNs) have been made in optical-X-ray bands. The results obtained so far show (i) random up and down on timescales longer than decades, (ii) no typical timescales of variability on shorter timescales and (iii) decreasing amplitudes as timescales become shorter. The second feature indicates that any successful model must produce a wide variety of shot-amplitudes and -durations over a few orders in their light curves. In this sense, we conclude that the disk instability model is favored over the starburst model, since fluctuations on days are hard to produce by the latter model.Inter-band correlations and time lags also impose great constraints on models. Thus, constructing wavelength and time dependent models remains as future work.

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20

Moon, Mi-Kyeong. "Variability Dependency Analysis for Generating Business Process Models based on Variability Decisions." KIPS Transactions:PartD 16D, no. 5 (October 31, 2009): 791–800. http://dx.doi.org/10.3745/kipstd.2009.16d.5.791.

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21

Alakoz, Gennady M., Alexander Vladimirovich Kotov, Morgan Madec, Sergey I. Plyaskota, Svetlana Mikhailovna Tolpygo, and Elizaveta Bydanova. "Sensory variability: rank models and neurophysiological prototypes." International Journal of System of Systems Engineering 12, no. 2 (2022): 150. http://dx.doi.org/10.1504/ijsse.2022.124978.

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22

Tolpygo, Svetlana Mikhailovna, Sergey I. Plyaskota, Morgan Madec, Alexander Vladimirovich Kotov, Gennady M. Alakoz, and Elizaveta Bydanova. "Sensory variability: rank models and neurophysiological prototypes." International Journal of System of Systems Engineering 12, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijsse.2022.10048860.

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23

STÖCKER, SABINE. "STABILITY DUE TO VARIABILITY IN DAISYWORLD MODELS." Journal of Biological Systems 03, no. 02 (June 1995): 331–39. http://dx.doi.org/10.1142/s0218339095000319.

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The Daisyworld is a mathematical model to describe a coupling between biosphere and climate. It was developed by J.E. Lovelock in context with the so-called Gaia-Hypothesis, which postulates that conditions on earth are suitable for life because of the existence of life itself. Some variations of the classical model will be discussed and so it can be shown, that the occurrence of mutations of the existing species will not harm the capability of regulating the system. Furthermore models provoking structures either in time or in space by self-organization will be presented. Such structures in time and space will enlarge the biological diversity and stabilize the system because of a more effective use of resources.

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24

Park, Bernadette, and Charles M. Judd. "Measures and models of perceived group variability." Journal of Personality and Social Psychology 59, no. 2 (1990): 173–91. http://dx.doi.org/10.1037/0022-3514.59.2.173.

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25

Radenen, Mathieu, and Thierry Artières. "Handling signal variability with contextual markovian models." Pattern Recognition Letters 35 (January 2014): 236–45. http://dx.doi.org/10.1016/j.patrec.2013.08.015.

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26

Ashburner, John, and Stefan Klöppel. "Multivariate models of inter-subject anatomical variability." NeuroImage 56, no. 2 (May 2011): 422–39. http://dx.doi.org/10.1016/j.neuroimage.2010.03.059.

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27

Burr, Robert L., and Marie J. Cowan. "Autoregressive spectral models of heart rate variability." Journal of Electrocardiology 25 (January 1992): 224–33. http://dx.doi.org/10.1016/0022-0736(92)90108-c.

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28

Behseta, Sam, Robert E. Kass, and Garrick L. Wallstrom. "Hierarchical models for assessing variability among functions." Biometrika 92, no. 2 (June 1, 2005): 419–34. http://dx.doi.org/10.1093/biomet/92.2.419.

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29

Hawkins, M. R. S. "Quasar Variability: New Surveys and New Models." International Astronomical Union Colloquium 184 (2002): 351–56. http://dx.doi.org/10.1017/s0252921100031006.

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AbstractIn this paper results from a monitoring programme of a large sample of quasars comprising regular yearly observations over a period of 23 years are presented. Structure functions of the light curves are calculated and compared with predictions for models of quasar variability of current interest. These include recently published models of varibility from accretion disk instability, variability from starbursts or supernovae, and variations caused by the microlensing effect of compact bodies along the line of sight. The analysis favours the accretion disk model for low luminosity AGN, but suggests that the variations of more luminous quasars are dominated by microlensing.

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30

Zerva, Aspasia. "Seismic loads predicted by spatial variability models." Structural Safety 11, no. 3-4 (December 1992): 227–43. http://dx.doi.org/10.1016/0167-4730(92)90016-g.

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31

Elston, D. A., and C. A. Glasbey. "Variability within system models: A case study." Agricultural Systems 37, no. 3 (January 1991): 309–18. http://dx.doi.org/10.1016/0308-521x(91)90039-d.

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32

Ford, Jessica H., Mark V. Bravington, and Jooke Robbins. "Incorporating individual variability into mark-recapture models." Methods in Ecology and Evolution 3, no. 6 (October 1, 2012): 1047–54. http://dx.doi.org/10.1111/j.2041-210x.2012.00243.x.

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33

Auer, Martin T., and Steven W. Effler. "Variability in Photosynthesis: Impact on DO Models." Journal of Environmental Engineering 115, no. 5 (October 1989): 944–63. http://dx.doi.org/10.1061/(asce)0733-9372(1989)115:5(944).

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34

Huppenkothen, Daniela, Brendon J. Brewer, David W. Hogg, Iain Murray, Marcus Frean, Chris Elenbaas, Anna L. Watts, Yuri Levin, Alexander J. van der Horst, and Chryssa Kouveliotou. "DISSECTING MAGNETAR VARIABILITY WITH BAYESIAN HIERARCHICAL MODELS." Astrophysical Journal 810, no. 1 (September 1, 2015): 66. http://dx.doi.org/10.1088/0004-637x/810/1/66.

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35

Phillips, Adam S., Clara Deser, and John Fasullo. "Evaluating Modes of Variability in Climate Models." Eos, Transactions American Geophysical Union 95, no. 49 (December 9, 2014): 453–55. http://dx.doi.org/10.1002/2014eo490002.

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36

Shimbara, Daisuke, Motoshi Saeki, Shinpei Hayashi, and Øystein Haugen. "Handling Quantity in Variability Models for System-of-Systems." International Journal of Software Engineering and Knowledge Engineering 31, no. 05 (May 2021): 693–724. http://dx.doi.org/10.1142/s0218194021500200.

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Problem: Modern systems contain parts that are themselves systems. Such complex systems thus have sets of subsystems that have their own variability. These subsystems contribute to the functionality of a whole system-of-systems (SoS). Such systems have a very high degree of variability. Therefore, a modeling technique for the variability of an entire SoS is required to express two different levels of variability: variability of the SoS as a whole and variability of subsystems. If these levels are described together, the model becomes hard to understand. When the variability model of the SoS is described separately, each variability model is represented by a tree structure and these models are combined in a further tree structure. For each node in a variability model, a quantity is assigned to express the multiplicity of its instances per one instance of its parent node. Quantities of the whole system may refer to the number of subsystem instances in the system. From the viewpoint of the entire system, constraints and requirements written in natural language are often ambiguous regarding the quantities of subsystems. Such ambiguous constraints and requirements may lead to misunderstandings or conflicts in an SoS configuration. Approach: A separate notion is proposed for variability of an SoS; one model considers the SoS as an undivided entity, while the other considers it as a combination of subsystems. Moreover, a domain-specific notation is proposed to express relationships among the variability properties of systems, to solve the ambiguity of quantities and establish the total validity. This notation adapts an approach, named Pincer Movement, which can then be used to automatically deduce the quantities for the constraints and requirements. Validation: The descriptive capability of the proposed notation was validated with four examples of cloud providers. In addition, the proposed method and description tool were validated through a simple experiment on describing variability models with real practitioners.

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37

Bányai, Mihály, Zsombor Koman, and Gergő Orbán. "Population activity statistics dissect subthreshold and spiking variability in V1." Journal of Neurophysiology 118, no. 1 (July 1, 2017): 29–46. http://dx.doi.org/10.1152/jn.00931.2016.

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Response variability, as measured by fluctuating responses upon repeated performance of trials, is a major component of neural responses, and its characterization is key to interpret high dimensional population recordings. Response variability and covariability display predictable changes upon changes in stimulus and cognitive or behavioral state, providing an opportunity to test the predictive power of models of neural variability. Still, there is little agreement on which model to use as a building block for population-level analyses, and models of variability are often treated as a subject of choice. We investigate two competing models, the doubly stochastic Poisson (DSP) model assuming stochasticity at spike generation, and the rectified Gaussian (RG) model tracing variability back to membrane potential variance, to analyze stimulus-dependent modulation of both single-neuron and pairwise response statistics. Using a pair of model neurons, we demonstrate that the two models predict similar single-cell statistics. However, DSP and RG models have contradicting predictions on the joint statistics of spiking responses. To test the models against data, we build a population model to simulate stimulus change-related modulations in pairwise response statistics. We use single-unit data from the primary visual cortex (V1) of monkeys to show that while model predictions for variance are qualitatively similar to experimental data, only the RG model's predictions are compatible with joint statistics. These results suggest that models using Poisson-like variability might fail to capture important properties of response statistics. We argue that membrane potential-level modeling of stochasticity provides an efficient strategy to model correlations. NEW & NOTEWORTHY Neural variability and covariability are puzzling aspects of cortical computations. For efficient decoding and prediction, models of information encoding in neural populations hinge on an appropriate model of variability. Our work shows that stimulus-dependent changes in pairwise but not in single-cell statistics can differentiate between two widely used models of neuronal variability. Contrasting model predictions with neuronal data provides hints on the noise sources in spiking and provides constraints on statistical models of population activity.

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38

Yeh, Sang‐Wook, Seung‐Hwon Hyun, In‐Hong Park, and Xiao‐Tong Zheng. "Surface temperature variability in climate models with large and small internal climate variability." Quarterly Journal of the Royal Meteorological Society 147, no. 738 (July 2021): 3004–16. http://dx.doi.org/10.1002/qj.4112.

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39

Solanki, Sami K. "Solar irradiance variability." Proceedings of the International Astronomical Union 2, no. 14 (August 2006): 279. http://dx.doi.org/10.1017/s1743921307010587.

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40

Zavala-Garay, J., C. Zhang, A. M. Moore, A. T. Wittenberg, M. J. Harrison, A. Rosati, Jérôme Vialard, and R. Kleeman. "Sensitivity of Hybrid ENSO Models to Unresolved Atmospheric Variability." Journal of Climate 21, no. 15 (August 1, 2008): 3704–21. http://dx.doi.org/10.1175/2007jcli1188.1.

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Abstract A common practice in the design of forecast models for ENSO is to couple ocean general circulation models to simple atmospheric models. Therefore, by construction these models (known as hybrid ENSO models) do not resolve various kinds of atmospheric variability [e.g., the Madden–Julian oscillation (MJO) and westerly wind bursts] that are often regarded as “unwanted noise.” In this work the sensitivity of three hybrid ENSO models to this unresolved atmospheric variability is studied. The hybrid coupled models were tuned to be asymptotically stable and the magnitude, and spatial and temporal structure of the unresolved variability was extracted from observations. The results suggest that this neglected variability can add an important piece of realism and forecast skill to the hybrid models. The models were found to respond linearly to the low-frequency part of the neglected atmospheric variability, in agreement with previous findings with intermediate models. While the wind anomalies associated with the MJO typically explain a small fraction of the unresolved variability, a large fraction of the interannual variability can be excited by this forcing. A large correlation was found between interannual anomalies of Kelvin waves forced by the intraseasonal MJO and the Kelvin waves forced by the low-frequency part of the MJO. That is, in years when the MJO tends to be more active it also produces a larger low-frequency contribution, which can then resonate with the large-scale coupled system. Other kinds of atmospheric variability not related to the MJO can also produce interannual anomalies in the hybrid models. However, when projected on the characteristics of Kelvin waves, no clear correlation between its low-frequency content and its intraseasonal activity was found. This suggests that understanding the mechanisms by which the intraseasonal MJO interacts with the ocean to modulate its low-frequency content may help to better to predict ENSO variability.

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41

Thiersen, Hannes, Michael Zacharias, and Markus Böttcher. "Simulations of Stochastic Long-term Variability in Leptonic Models for External-Compton and Synchrotron Self-Compton Dominated Blazars." Astrophysical Journal 925, no. 2 (February 1, 2022): 177. http://dx.doi.org/10.3847/1538-4357/ac4013.

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Abstract In this work we investigate the nature of multiwavelength variability of blazars from a purely numerical approach. We use a time-dependent one-zone leptonic blazar emission model to simulate multiwavelength variability by introducing stochastic parameter variations in the emission region. These stochastic parameter variations are generated by Monte Carlo methods and have a characteristic power-law index of α = −2 in their power spectral densities. We include representative blazar test cases for a flat spectrum radio quasar and a high-synchrotron peaked BL Lacertae object for which the high-energy component of the spectral energy distribution is dominated by external-Compton and synchrotron self-Compton emission, respectively. The simulated variability is analyzed in order to characterize the distinctions between the two blazar cases and the physical parameters driving the variability. We show that the variability’s power spectrum is closely related to underlying stochastic parameter variations for both cases. Distinct differences between the different progenitor variations are present in the multiwavelength cross-correlation functions.

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42

Nešetřilová, H. "Multiphasic growth models for cattle." Czech Journal of Animal Science 50, No. 8 (December 11, 2011): 347–54. http://dx.doi.org/10.17221/4176-cjas.

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There are several ways of generalizing classical growth models to describe the complex nature of animal growth. One possibility is to construct a model based on a sum of several classical growth functions. In this paper, such multiphasic growth models for breeding bulls of the Czech Pied cattle based on the sum of two logistic functions are studied. The logistic function was chosen as a base for the models due to the relatively low degree of nonlinearity for the growth data. The paper describes three steps of constructing such a multiphasic growth model: in the first step a model with four unknown parameters is considered, in the second step the number of model parameters which are to be estimated is increased to five and in the third step a general model with six parameters is used. In each step, statistical properties of the considered model are checked. The residual variability of the best fitting model is on average approx. 8 times lower than the residual variability of classical Gompertz model which is often used by breeders to model cattle growth.  

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43

Perry, J. N. "Some Models for Spatial Variability of Animal Species." Oikos 51, no. 2 (February 1988): 124. http://dx.doi.org/10.2307/3565634.

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44

Ball, Frank, and Peter Donnelly. "A Unified Approach to Variability in Compartmental Models." Biometrics 44, no. 3 (September 1988): 685. http://dx.doi.org/10.2307/2531583.

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45

Moon, Mi-Kyeong, and Keun-Hyuk Yeom. "Variability Analysis Approach for Business Process Family Models." KIPS Transactions:PartD 15D, no. 5 (October 31, 2008): 621–28. http://dx.doi.org/10.3745/kipstd.2008.15-d.5.621.

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46

Nestler, Steffen, Katharina Geukes, and Mitja D. Back. "Modeling Intraindividual Variability in Three-Level Multilevel Models." Methodology 14, no. 3 (July 1, 2018): 95–108. http://dx.doi.org/10.1027/1614-2241/a000150.

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Abstract. The mixed-effects location scale model is an extension of a multilevel model for longitudinal data. It allows covariates to affect both the within-subject variance and the between-subject variance (i.e., the intercept variance) beyond their influence on the means. Typically, the model is applied to two-level data (e.g., the repeated measurements of persons), although researchers are often faced with three-level data (e.g., the repeated measurements of persons within specific situations). Here, we describe an extension of the two-level mixed-effects location scale model to such three-level data. Furthermore, we show how the suggested model can be estimated with Bayesian software, and we present the results of a small simulation study that was conducted to investigate the statistical properties of the suggested approach. Finally, we illustrate the approach by presenting an example from a psychological study that employed ecological momentary assessment.

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47

Ghisellini, G., I. M. George, and C. Done. "Frequency-dependent variability in synchrotron self-Compton models." Monthly Notices of the Royal Astronomical Society 241, no. 1 (November 1, 1989): 43P—49P. http://dx.doi.org/10.1093/mnras/241.1.43p.

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48

Bahamonde-Birke, Francisco J., and Juan de Dios Ortúzar. "On the variability of hybrid discrete choice models." Transportmetrica A: Transport Science 10, no. 1 (July 16, 2012): 74–88. http://dx.doi.org/10.1080/18128602.2012.700338.

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49

Martin, Thomas, Annika Reintges, and Mojib Latif. "Coupled North Atlantic Subdecadal Variability in CMIP5 Models." Journal of Geophysical Research: Oceans 124, no. 4 (April 2019): 2404–17. http://dx.doi.org/10.1029/2018jc014539.

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50

Davis, John D., Suzanne Rivoire, Moises Goldszmidt, and Ehsan K. Ardestani. "Including Variability in Large-Scale Cluster Power Models." IEEE Computer Architecture Letters 11, no. 2 (July 2012): 29–32. http://dx.doi.org/10.1109/l-ca.2011.27.

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