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Książki na temat „Multi-model model”

Autor: Grafiati
Data publikacji: 6 września 2023

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1

Anastasopoulos, Achilles. Cross model access in the multi-lingual, multi-model database management system. Monterey, Calif: Naval Postgraduate School, 1997.

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2

Pistikopoulos, Efstratios N., Michael C. Georgiadis i Vivek Dua, red. Multi-Parametric Model-Based Control. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2007. http://dx.doi.org/10.1002/9783527631230.

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3

Lindgren, Peter. The Multi Business Model Innovation Approach. New York: River Publishers, 2022. http://dx.doi.org/10.1201/9781003339755.

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4

Fund, International Monetary. MULTIMOD: A multi-region econometric model. Washington, D.C: International Monetary Fund, 1988.

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5

Introduction to multi-disciplinary model-building. Southampton, UK: WIT Press, 2003.

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6

Powers, M. W. Multi-modal modelling with multi-module mechanics: Autonomy in acomputational model of language learning. Tilburg: Tilburg University, 1992.

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7

Stam, Jos. A multi-scale stochastic model for computer graphics. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1991.

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8

Pusat Informatik Untuk Pengelolaan Pendidikan dan Kebudayaan (Indonesia), red. Model tayangan multi media Departemen Pendidikan dan Kebudayaan. [Jakarta]: Pusat Informatik Untuk Pengelolaan Pendidikan dan Kebudayaan, Badan Penelitian dan Pengembangan Pendidikan dan Kebudayaan, Departemen Pendidikan dan Kebudayaan, 1998.

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9

(Japan), Ajia Keizai Kenkyūjo. Multi-regional input-output model for China 2000. Chiba-shi, Japan: Institute of Developing Economies, Japan External Trade Organization, 2003.

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10

C, Georgiadis Michael, Pistikopoulos Efstratios N i Dua Vivek, red. Multi-parametric model-based control: Theory and applications. Weinheim: Wiley-VCH, 2007.

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11

Bunt, H. C. A model-theoretic approach to multi-database knowledgerepresentation. Tilburg: Tilburg University, 1989.

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12

Emotionality and mental illness: A multi-dimensional model. Hauppauge, N.Y: Nova Science Publishers, 2011.

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13

Stam, Jos. A multi-scale stochastic model for computer graphics. Toronto: University of Toronto, Dept. of Computer Science, 1991.

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14

Stewart, Dianne. A model of a multi-product bank with multi-economies of scale. London: Department of Economics, School of Oriental and African Studies, University of London, 1992.

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15

Chapman, Barbara, Weiming Zheng, Guang R. Gao, Mitsuhisa Sato, Eduard Ayguadé i Dongsheng Wang, red. A Practical Programming Model for the Multi-Core Era. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69303-1.

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16

Di Nitto, Elisabetta, Peter Matthews, Dana Petcu i Arnor Solberg, red. Model-Driven Development and Operation of Multi-Cloud Applications. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46031-4.

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17

He, Shuping, i Xiaoli Luan. Multi-model Jumping Systems: Robust Filtering and Fault Detection. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6474-5.

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18

Ngai, L. Rachel. Structural change in a multi-sector model of growth. Bonn, Germany: IZA, 2005.

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19

Mao, K. Z. Multi-directional model validity tests for nonlinear system identification. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1997.

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20

Australian Institute of Health and Welfare. Indigenous housing needs 2005: A multi-measure needs model. Canberra: Australian Institute of Health and Welfare, 2005.

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21

Petrie, Melinda. Indigenous housing needs 2009: A multi-measure needs model. Canberra: Australian Institute of Health and Welfare, 2009.

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22

Ngai, Liwa Rachel. An R&D-based model of multi-sector growth. London: Centre for Economic Performance, London School of Economics and Political Science, 2006.

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23

Bhattacharya, Prabir C. A multi-sector model of employment, migration and development. London: University College, 1987.

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24

Zyda, Michael J. A data structure for a multi-illumination model renderer. Monterey, California: Naval Postgraduate School, 1986.

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25

Ittai, Gavrieli, i Makhon ha-geʼologi (Israel), red. A multi-component chemistry-based model for the Dead Sea: Modifications of the 1D Princeton Oceanographic Model. Jerusalem: State of Israel, Ministry of National Infrastructures, Geological Survey of Israel, 2006.

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26

Ittai, Gavrieli, i Makhon ha-geʼologi (Israel), red. A multi-component chemistry-based model for the Dead Sea: Modifications of the 1D Princeton Oceanographic Model. Jerusalem: State of Israel, Ministry of National Infrastructures, Geological Survey of Israel, 2006.

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27

Ittai, Gavrieli, i Makhon ha-geʼologi (Israel), red. A multi-component chemistry-based model for the Dead Sea: Modifications of the 1D Princeton Oceanographic Model. Jerusalem: State of Israel, Ministry of National Infrastructures, Geological Survey of Israel, 2006.

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28

Prowse, Victoria. State dependence in a multi-state model of employment dynamics. Bonn, Germany: IZA, 2005.

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29

Hakala, Tuula. A stochastic optimization model for multi-currency bond portfolio management. Helsinki: Helsinki School of Economics and Business Administration, 1996.

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30

Chang, C. T. A Model of Pellet Ablation with a Multi-Species Ablatant. Roskilde, Denmark: Riso National Laboratory, 1988.

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31

Water Resources Research Laboratory (U.S.), red. Glen Canyon Dam multi-level intake structure hydraulic model study. Denver, Colo: U.S. Dept. of Interior, Bureau of Reclamation, Technical Service Center, Water Resources Services, Water Resources Research Laboratory, 1999.

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32

Archibald, T. W. An aggregate stochastic dynamic programming model of multi-reservoir systems. Edinburgh: University of Edinburgh, Management School, 1996.

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33

Chen, J. Quality assurance of multi-media model for predictive screening tests. Washington, DC: U.S. Environmental Protection Agency, Office of Research and Development, 1999.

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34

Fund, International Monetary. Effects of long-run demographic changes in a multi-country model. Washington, D.C: International Monetary Fund, 1991.

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35

Legoh, Finarya. Acoustic design and scale model testing of a multi-purpose auditorium. Salford: University of Salford, 1988.

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36

Lee, Kevin. Growth and convergence in a multi-country empirical stochastic Solow model. Cairo: Economic Research Forum for the Arab Countries, Iran & Turkey, 1996.

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37

Lockheed, Marlaine E. A multi-level model of school effectiveness in a developing country. Washington, DC (1818 H. St. NW, Washington 20433): Policy, Planning, and Research Complex, World Bank, 1989.

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38

Börsch-Supan, Axel. Aging, pension reform, and capital flows: A multi-country simulation model. Cambridge, Mass: National Bureau of Economic Research, 2005.

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39

Burnham, Kenneth P., i David Anderson. Model Selection and Multi-Model Inference. Springer, 2003.

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40

Cross Model Access in the Multi-Lingual, Multi-Model Database Management System. Storming Media, 1997.

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41

Multi-parametric model-based control. Weinheim: Wiley-VCH, 2006.

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42

Multi Business Model Innovation Approach. River Publishers, 2018.

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43

Sanderson, Benjamin Mark. Uncertainty Quantification in Multi-Model Ensembles. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.707.

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Streszczenie:
Long-term planning for many sectors of society—including infrastructure, human health, agriculture, food security, water supply, insurance, conflict, and migration—requires an assessment of the range of possible futures which the planet might experience. Unlike short-term forecasts for which validation data exists for comparing forecast to observation, long-term forecasts have almost no validation data. As a result, researchers must rely on supporting evidence to make their projections. A review of methods for quantifying the uncertainty of climate predictions is given. The primary tool for quantifying these uncertainties are climate models, which attempt to model all the relevant processes that are important in climate change. However, neither the construction nor calibration of climate models is perfect, and therefore the uncertainties due to model errors must also be taken into account in the uncertainty quantification.Typically, prediction uncertainty is quantified by generating ensembles of solutions from climate models to span possible futures. For instance, initial condition uncertainty is quantified by generating an ensemble of initial states that are consistent with available observations and then integrating the climate model starting from each initial condition. A climate model is itself subject to uncertain choices in modeling certain physical processes. Some of these choices can be sampled using so-called perturbed physics ensembles, whereby uncertain parameters or structural switches are perturbed within a single climate model framework. For a variety of reasons, there is a strong reliance on so-called ensembles of opportunity, which are multi-model ensembles (MMEs) formed by collecting predictions from different climate modeling centers, each using a potentially different framework to represent relevant processes for climate change. The most extensive collection of these MMEs is associated with the Coupled Model Intercomparison Project (CMIP). However, the component models have biases, simplifications, and interdependencies that must be taken into account when making formal risk assessments. Techniques and concepts for integrating model projections in MMEs are reviewed, including differing paradigms of ensembles and how they relate to observations and reality. Aspects of these conceptual issues then inform the more practical matters of how to combine and weight model projections to best represent the uncertainties associated with projected climate change.
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44

Ermolaev, Igor, Jared Laxton i Charles Freedman. Small Quarterly Multi-Country Projection Model. International Monetary Fund, 2008.

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45

Ermolaev, Igor, Jared Laxton i Charles Freedman. Small Quarterly Multi-Country Projection Model. International Monetary Fund, 2008.

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46

Carabenciov, Ioan, Igor Ermolaev, Jared Laxton, Charles Freedman i Michel Juillard. Small Quarterly Multi-Country Projection Model. International Monetary Fund, 2008.

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47

Lindgren, Peter. Multi Business Model Innovation Approach: Part 1. River Publishers, 2018.

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48

Corenelius mission model: In multi-faith context. Delhi: Indian Society for Promoting Christian Knowledge, 2021.

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49

Gassman, Natalie R. Bisphenol A: A Multi-Model Endocrine Disruptor. Royal Society of Chemistry, The, 2021.

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50

Lindgren, Peter. Multi Business Model Innovation Approach: Part 1. River Publishers, 2022.

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