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Published: 14 March 2023

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1

Henzinger, T. A. An interleaving model for real time. Stanford, Calif: Dept. of Computer Science, Stanford University, 1990.

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2

Dailey, Daniel J. A cellular automata model for use with real freeway data. [Olympia, Wash.]: Washington State Dept. of Transportation, 2002.

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3

Carroll, Jeremy J. A deterministic model of time for distributed systems. Palo Alto, CA: Hewlett-Packard Laboratories, Technical Publications Department, 1996.

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4

Savola, Reijo. The model execution kernel of a real-time software prototyping environment. Espoo [Finland]: Technical Research Centre of Finland, 1992.

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5

Verghese, Gilbert. Perspective alignment back-projection for real-time monocular three-dimensional model-based computer vision. Toronto: Dept. of Computer Science, University of Toronto, 1995.

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6

Galí, Jordi. Technology shocks and aggregate fluctuations: How well does the real business cycle model fit postwar U.S. data? [Washington, D.C]: International Monetary Fund, Western Hemisphere Dept., 2004.

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7

Johnson, Steven A. A simple dynamic engine model for use in a real-time aircraft simulation with thrust vectoring. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Division, 1990.

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8

Crawford, Jason A. Modal emissions modeling with real traffic data. College Station, Tex: Texas Transportation Institute, Texas A&M University System, 1999.

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9

United States. National Aeronautics and Space Administration., ed. Real-time sensor data validation. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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10

United States. National Aeronautics and Space Administration., ed. Real-time sensor data validation. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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11

United States. National Aeronautics and Space Administration., ed. Real-time sensor data validation. [Washington, DC]: National Aeronautics and Space Administration, 1994.

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12

Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.
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13

Jean, Boivin. Has US monetary policy changed?: Evidence from drifting coefficients and real-time data. Cambridge, MA: National Bureau of Economic Research, 2005.

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14

Jean, Boivin. Has US monetary policy changed?: Evidence from drifting coefficients and real-time data. Cambridge, Mass: National Bureau of Economic Research, 2005.

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15

1953-, Kirćanski N., ed. Real-time dynamics of manipulation robots. Berlin: Springer-Verlag, 1985.

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16

Jajodia, Sushil. Advanced Transaction Models and Architectures. Boston, MA: Springer US, 1997.

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17

Evans, Martin D. D. Where are we now?: Real-time estimates of the macro economy. Cambridge, Mass: National Bureau of Economic Research, 2005.

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18

Evans, Martin D. D. Where are we now?: Real-time estimates of the macro economy. Cambridge, MA: National Bureau of Economic Research, 2005.

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19

Mednick, Adam C. Accessing online data for building and evaluating real-time models to predict beach water quality. Madison, WI: Bureau of Science Services, Wisconsin Department of Natural Resources, 2009.

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20

Belenʹkiĭ, V. R. Rynochnai︠a︡ t︠s︡ena zemelʹno-imushchestvennykh obʺektov (kompleksov): Uchebnoe posobie. Moskva: Bylina, 2001.

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21

N, Perry Joe, ed. Chaos in real data: The analysis of non-linear dynamics from short ecological time series. Dordrecht: Kluwer Academic Publishers, 2000.

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22

White, Josh. Designing 3D graphics: How to create real-time 3D models for games and virtual reality. New York: Wiley Computer Pub., 1996.

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23

P, Gomes Abel J., ed. Implicit curves and surfaces: Mathematics, data structures, and algorithms. Dordrecht: Springer-Verlag, 2009.

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24

Robert, Fuller, and SpringerLink (Online service), eds. Possibility for Decision: A Possibilistic Approach to Real Life Decisions. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.

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25

Luttmer, Erzo F. P. Measuring poverty dynamics and inequality in transition economies: Disentangling real events from noisy data. Washington, DC: World Bank, Europe and Central Asia Region, Poverty Reduction and Economic Management Sector Unit, 2001.

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26

Luttmer, Erzo F. P. Measuring poverty dynamics and inequality in transition economies: Disentangling real events from noisy data. Washington, DC: World Bank, Europe and Central Asia Region, Poverty Reduction and Economic Management Sector Unit, 2001.

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27

Peter, Koch. Zur Makroprozessmodellierung für die Echtzeitprozessführung in der Flotation. Leipzig: Deutscher Verlag für Grundstoffindustrie, 1990.

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28

Computational chemistry: A practical guide for applying techniques to real world problems. New York: Wiley, 2001.

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29

Buscema, Massimo. Intelligent Data Mining in Law Enforcement Analytics: New Neural Networks Applied to Real Problems. Dordrecht: Springer Netherlands, 2013.

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30

Yoon, Sung-eui, Enrico Gobbetti, Dinesh Manocha, and David Kasik. Real-Time Massive Model Rendering. Morgan & Claypool Publishers, 2008.

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31

Yoon, Sung-eui, Enrico Gobbetti, Dinesh Manocha, and David Kasik. Real-Time Massive Model Rendering. Morgan & Claypool Publishers, 2008.

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32

Yoon, Sung-eui, Enrico Gobbetti, Dinesh Manocha, and David Kasik. Real-Time Massive Model Rendering. Springer International Publishing AG, 2008.

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33

Dulay, Hubert. Streaming Data Mesh: A Model for Optimizing Real-Time Data Services. O'Reilly Media, Incorporated, 2023.

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34

Baird, Ward. Markov Decision Process : Makov Model in Real Data Collection: How to Evaluate Markov Model. Independently Published, 2021.

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35

Babau, Jean-Philippe, Mireille Blay-Fornarino, Jöel Champeau, Sylvain Robert, and Antonino Sabetta. Model Driven Engineering for Distributed Real-Time Embedded Systems 2009: Advances, Standards, Applications and Perspectives. Wiley & Sons, Incorporated, John, 2013.

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36

Champeau, J., Jean-Philippe Babau, Mireille Blay-Fornarino, Sylvain Robert, and Antonino Sabetta. Model Driven Engineering for Distributed Real-Time Embedded Systems 2009: Advances, Standards, Applications and Perspectives. Wiley & Sons, Incorporated, John, 2013.

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37

Babau, Jean-Philippe, Mireille Blay-Fornarino, Jöel Champeau, Sylvain Robert, and Antonino Sabetta. Model Driven Engineering for Distributed Real-Time Embedded Systems 2009: Advances, Standards, Applications and Perspectives. Wiley & Sons, Incorporated, John, 2010.

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38

Babau, Jean-Philippe, Mireille Blay-Fornarino, Jöel Champeau, Sylvain Robert, and Antonino Sabetta. Model Driven Engineering for Distributed Real-Time Embedded Systems 2009: Advances, Standards, Applications and Perspectives. Wiley & Sons, Incorporated, John, 2013.

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39

Babau, Jean-Philippe, Mireille Blay-Fornarino, Jöel Champeau, Sylvain Robert, and Antonino Sabetta. Model Driven Engineering for Distributed Real-Time Embedded Systems 2009: Advances, Standards, Applications and Perspectives. Wiley & Sons, Incorporated, John, 2013.

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40

Real-time rainfall-runoff model of the Carraízo-Reservoir Basin in Puerto Rico. San Juan, P.R. (651 Federal Drive, San Juan 00965): Dept. of the Interior, U.S. Geological Survey, 1996.

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41

Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Capstone: Application of NLTS to Real-World Data. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0010.

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This chapter investigates the use of Extreme Value Statistics (EVS) to probabilistically model extreme events separated as unstructured noise in signal processing. We apply a version of EVS that computes the likelihood of extreme discrepancies exceeding a selected threshold value within a given time interval. In theory, exceedances follow a Generalized Pareto (GP) distribution, and we run diagnostics to determine how well the data actually fit this distribution. If we find a reasonable fit, we can invert the GP distribution to solve for quantiles providing a useful noise diagnostic: return level plots. Return level plots show the return periods expected before particular extreme noise levels return levels are realized.
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42

Verghese, Gilbert. Perspective alignment back-projection for real-time monocular three-dimensional model-based computer vision. 1995.

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43

Plotnikov, Dmitry. What Prevents a Real Business Cycle Model from Matching the U. S. Data? Decomposing the Labor Wedge. International Monetary Fund, 2017.

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44

Plotnikov, Dmitry. What Prevents a Real Business Cycle Model from Matching the U. S. Data? Decomposing the Labor Wedge. International Monetary Fund, 2017.

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45

Plotnikov, Dmitry. What Prevents a Real Business Cycle Model from Matching the U. S. Data? Decomposing the Labor Wedge. International Monetary Fund, 2017.

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46

Modeldriven Engineering For Distributed Realtime Systems Marte Modeling Model Transformations And Their Usages. Wiley-Iste, 2010.

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47

On Data Mining in Context: Cases, Fusion and Evaluation. Leiden, The Netherlands: Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University, 2010.

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48

Cai, Zongwu. Functional Coefficient Models for Economic and Financial Data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.6.

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This article discusses the use of functional coefficient models for economic and financial data analysis. It first provides an overview of recent developments in the nonparametric estimation and testing of functional coefficient models, with particular emphasis on the kernel local polynomial smoothing method, before considering misspecification testing as an important econometric question when fitting a functional (varying) coefficient model or a trending time-varying coefficient model. It then describes two major real-life applications of functional coefficient models in economics and finance: the first deals with the use of functional coefficient instrumental-variable models to investigate the empirical relation between wages and education in a random sample of young Australian female workers from the 1985 wave of the Australian Longitudinal Survey, and the second is concerned with the use of functional coefficient beta models to analyze the common stock price of Microsoft stock (MSFT) during the year 2000 using the daily closing prices.
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49

Cheng, Russell. Embedded Model Problem. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0005.

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This chapter introduces embedded models. This is a special case of a parametric model which cannot be obtained simply by setting the parameters to particular values in a simple way. An example is the regression function y = b[1−exp(−ax)], which is always curved when a and b have fixed values. But letting a tend to zero and b tend to infinity simultaneously, whilst keeping ab = c fixed, yields y = cx, a straight-line special case. When this is the true model, fitting the original two-parameter model leads to very unstable and individually meaningless estimates of a and b. Such embedded models are actually very common in the literature, leading to confusion in interpretation of results when undetected. In this chapter, embeddedness is defined and a large number of regression embedded model examples given. Detection and removal of embeddedness by reparametrization is discussed. Two real data numerical examples are given.
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50

Laver, Michael, and Ernest Sergenti. Using Theoretical Models to Analyze Real Party Systems. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0011.

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Having specified theoretical models of multiparty competition in the first ten chapters of the book, this chapter analyzes recent party competition in postwar democracies in order to verify whether the empirical implications of the party competition model can indeed be systematically observed in real party competition. This is easy to say but hard to do in a rigorous way. Fundamental difficulties arise from two distinct sources. The first concerns calibration of key parameters of the model to the real political environments it is used to analyze. The second concerns data, specifically the need for reliable empirical observations of the real world that can be compared with theoretical implications of our model. The chapter discusses these two methodological problems before moving on to compare empirical implications generated by the model, calibrated to real party systems, with empirical observations of these same party systems.
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