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Books on the topic 'Graphical Modeler'

Author: Grafiati
Published: 29 June 2021
Last updated: 14 February 2022

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1

Robinson, Stephen. "Multiresolution analysis of polygonal surfaces" graphics modeller. Manchester: University of Manchester, Department of Computer Science, 1996.

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2

Kasʹi︠a︡nov, V. N. Vizualizat︠s︡ii︠a︡ grafov i grafovykh modeleĭ. Novosibirsk: Sibirskoe nauch. izd-vo, 2010.

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3

Ferraro, Richard F. A tutorial guide to PT/Modeler 2.0 and Pro/ENGINEER. Reading, Mass: Addison-Wesley, 1998.

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4

Computer graphics and geometric modeling. New York: Springer, 1999.

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5

Koepke, Marguerite L. Model graphics: Building and using study models. New York: Van Nostrand Reinhold, 1988.

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6

Whittaker, J. Graphical models in applied multivariate statistics. Chichester [England]: Wiley, 1990.

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7

How computer graphics work. Emeryville, Calif: Ziff-Davis Press, 1994.

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8

Barzel, Ronen. Physically-based modeling for computer graphics: A structured approach. Boston: Academic Press, 1992.

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9

Physically-based modeling for computer graphics: A structured approach. Boston: Academic Press, 1992.

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10

Engineering design communication and modeling using Unigraphics NX. New York: Thomson Delmar Learning, 2005.

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11

Computer graphics and geometric modeling for engineers. New York: J. Wiley, 1993.

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12

Smith, Brad Lee. Initial Graphics Exchange Specification (IGES), version 3.0. Warrendale, Pa: Society of Automotive Engineers, 1986.

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13

Integrated graphic and computer modelling. London: Springer, 2008.

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14

Engineering design graphics with Autodesk Inventor 2011. Boston: Prentice Hall, 2012.

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15

Bashkatov, Alexander. Modeling in OpenSCAD: examples. ru: INFRA-M Academic Publishing LLC., 2019. http://dx.doi.org/10.12737/959073.

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The tutorial is an introductory course to the study of the basics of geometric modeling for 3D printing using the programming language OpenSCAD and is built on the basis of descriptions of instructions for creating primitives, determining their properties, carrying out transformations and other service operations. It contains a large number of examples with detailed comments and description of the performed actions, which allows you to get basic skills in creating three-dimensional and flat models, exporting and importing graphical data. Meets the requirements of the Federal state educational standards of higher education of the last generation. It can be useful for computer science teachers, students, students and anyone who is interested in three-dimensional modeling and preparation of products for 3D printing.
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16

Go faster: The graphic design of racing cars. Berlin: Gestalten, 2009.

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17

Color theory and modeling for computer graphics, visualization, and multimedia applications. Boston: Kluwer Academic Publishers, 1997.

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18

An integrated introduction to computer graphics and geometric modeling. Boca Raton: Taylor & Francis, 2009.

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19

Engineering design and graphics with Autodesk Inventor 2009. Upper Saddle River, NJ: Pearson Education, 2009.

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20

Bethune, James D. Engineering design and graphics with Autodesk Inventor 10. Upper Saddle River, NJ: Pearson Prentice Hall, 2006.

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21

Engineering design and graphics with Autodesk Inventor 2008. Upper Saddle River, NJ: Pearson Prentice Hall, 2008.

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22

Brewe, David E. Study of journal bearing dynamics using three-dimensional motion picture graphics. [Cleveland, Ohio: National Aeronautics and Space Administration, Lewis Research Center, 1986.

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23

Physics-based deformable models: Applications to computer vision, graphics, and medical imaging. Boston: Kluwer Academic, 1997.

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24

Banff International Research Station for Mathematics Innovation & Discovery, ed. Mathematical foundations of scientific visualization, computer graphics, and massive data exploration. Berlin: Springer, 2009.

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25

Saufley, Ted. AutoCAD AME: Solid modeling for mechanical design. South Holland, Ill: Goodheart-Wilcox, 1994.

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26

McFarlane, Robert. Solid modelling with AutoCAD. London: E. Arnold, 1995.

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27

McFarlane, Robert. Modelling with AutoCAD release 13 for Windows. London: Arnold, 1997.

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28

McFarlane, Robert. Modelling with AutoCAD release 14 for Windows NT and Windows 95. London: Arnold, 1999.

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29

Koh, Eng-Kiat. Generating and structuring terrain features on parallel vector architectures. Urbana, IL (1304 W. Springfield Ave., Urbana 61801): Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1988.

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30

Derek, Miers, ed. BPMN modeling and reference guide: Understanding and using BPMN : develop rigorous yet understandable graphical representations of business processes. Lighthouse Point, Fla: Future Strategies Inc., 2008.

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31

Metaxas, Dimitris N. Physics-Based Deformable Models: Applications to Computer Vision, Graphics and Medical Imaging. Boston, MA: Springer US, 1997.

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32

The association graph and the multigraph for loglinear models. Thousand Oaks, Calif: SAGE, 2011.

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33

A, Segel Lee, and Segel Lee A, eds. Biograph: A graphical simulation package with exercises : to accompany Lee A. Segel's Modeling dynamic phenomena in molecular and cellular biology. Cambridge: Cambridge University Press, 1987.

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34

Using mathematics in economics. 2nd ed. Essex, England: Addison-Wesley, 1999.

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35

Bu que ding tu shu ju wa jue. Haerbin: Ha er bin gong ye da xue chu ban she, 2013.

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36

Heuermann, Arnulf. Die Erfahrungskurve im Telekommunikationsbereich. Berlin: Springer-Verlag, 1989.

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37

Lamden, M. J. Portable graphics for Modeller-using GKS. Bradford, 1987.

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38

Inc, Spatial Technology, ed. ACIS geometric modeler: Application guide. Boulder, Colo. (2425 55th St. Bldg A., Boulder 80301-5704): Spatial Technology, Inc., 1996.

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39

Adamson, Gary, and Brendan Bunting. Some statistical and graphical strategies for exploring the effect of interventions in health research. Oxford University Press, 2015. http://dx.doi.org/10.1093/med:psych/9780198527565.003.0021.

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This chapter explores statistical and graphical strategies for exploring the effect of interventions in health research and presents a brief overview of some common and some not so common methods for assessing data from interventions and have pointed to some associated advantages and disadvantages. It discusses gain scores, analysis of variance models, structural equation modelling, and the latent growth model.
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40

Practical R for biologists: an introduction. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0000.

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Abstract R is an open-source statistical environment modelled after the previously widely used commercial programs S and S-Plus, but in addition to powerful statistical analysis tools, it also provides powerful graphics outputs. In addition to its statistical and graphical capabilities, R is a programming language suitable for medium-sized projects. This book presents a set of studies that collectively represent almost all the R operations that beginners, analysing their own data up to perhaps the early years of doing a PhD, need. Although the chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping and text parsing, examples have been chosen based largely on real scientific studies at the appropriate level and within each the use of more R functions is nearly always covered than are simply necessary just to get a p-value or a graph. R comes with around a thousand base functions which are automatically installed when R is downloaded. This book covers the use of those of most relevance to biological data analysis, modelling and graphics. Throughout each chapter, the functions introduced and used in that chapter are summarized in Tool Boxes. The book also shows the user how to adapt and write their own code and functions. A selection of base functions relevant to graphics that are not necessarily covered in the main text are described in Appendix 1, and additional housekeeping functions in Appendix 2.
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41

(Illustrator), Gregg Coyle, ed. Model Graphics. Van Nostrand Reinhold/co Wiley, 1988.

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42

Bollen, Kenneth A., Sophia Rabe‐Hesketh, and Anders Skrondal. Structural Equation Models. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0018.

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This article explains the use of factor analysis types of models to develop measures of latent concepts which were then combined with causal models of the underlying latent concepts. In particular, it offers an overview of the classic structural equation models (SEMs) when the latent and observed variables are continuous. Then it looks at more recent developments that include categorical, count, and other noncontinuous variables as well as multilevel structural equation models. The model specification, assumptions, and notation are covered. This is followed by addressing implied moments, identification, estimation, model fit, and respecification. The penetration of SEMs has been high in disciplines such as sociology, psychology, educational testing, and marketing, but lower in economics and political science despite the large potential number of applications. Today, SEMs have begun to enter the statistical literature and to re-enter biostatistics, though often under the name ‘latent variable models’ or ‘graphical models’.
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43

Probabilistic Graphical Models for Genetics, Genomics and Postgenomics. Oxford University Press, 2014.

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44

Makela, Susanna, Yajuan Si, and Andrew Gelman. Graphical Visualization of Polling Results. Edited by Lonna Rae Atkeson and R. Michael Alvarez. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190213299.013.12.

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This chapter argues that it is wasteful to do a large, expensive poll and then just report a few percentages. Statistical modeling allows researchers to make the most effective use of available data, and graphs make it possible to convey more information more directly, both to general audiences and to specialists. Graphs are an invaluable tool at each step of the modeling process: exploring raw data, building and refining the model, and understanding and communicating the results are all made easier with graphs. In addition, graphical methods can be useful to survey researchers to understand weighting and other aspects of survey construction and analysis. The chapter includes several examples.
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45

McCleary, Richard, David McDowall, and Bradley Bartos. Design and Analysis of Time Series Experiments. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.001.0001.

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Design and Analysis of Time Series Experiments develops a comprehensive set of models and methods for drawing causal inferences from time series. Example analyses of social, behavioral, and biomedical time series illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. The classic Box-Jenkins-Tiao model-building strategy is supplemented with recent auxiliary tests for transformation, differencing, and model selection. The validity of causal inferences is approached from two complementary directions. The four-validity system of Cook and Campbell relies on ruling out discrete threats to statistical conclusion, internal, construct, and external validity. The Rubin system causal model relies on the identification of counterfactual time series. The two approaches to causal validity are shown to be complementary and are illustrated with a construction of a synthetic control time series. Example analyses make optimal use of graphical illustrations. Mathematical methods used in the example analyses are explicated in technical appendices, including expectation algebra, sequences and series, maximum likelihood, Box-Cox transformation analyses and probability.
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46

McDowall, David, Richard McCleary, and Bradley J. Bartos. Interrupted Time Series Analysis. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190943943.001.0001.

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Interrupted Time Series Analysis develops a comprehensive set of models and methods for drawing causal inferences from time series. Example analyses of social, behavioural, and biomedical time series illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. The classic Box-Jenkins-Tiao model-building strategy is supplemented with recent auxiliary tests for transformation, differencing and model selection. New developments, including Bayesian hypothesis testing and synthetic control group designs are described and their prospects for widespread adoption are discussed. Example analyses make optimal use of graphical illustrations. Mathematical methods used in the example analyses are explicated assuming only exposure to an introductory statistics course. Design and Analysis of Time Series Experiments (DATSE) and other appropriate authorities are cited for formal proofs. Forty completed example analyses are used to demonstrate the implications of model properties. The example analyses are suitable for use as problem sets for classrooms, workshops, and short-courses.
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47

Wittman, David M. The Elements of Relativity. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199658633.001.0001.

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Relativity is a set of remarkable insights into the way space and time work. The basic notion of relativity, first articulated by Galileo, explains why we do not feel Earth moving as it orbits the Sun and was successful for hundreds of years. We present thinking tools that elucidate Galilean relativity and prepare us for the more modern understanding. We then show how Galilean relativity breaks down at speeds near the speed of light, and follow Einstein’s steps in working out the unexpected relationships between space and time that we now call special relativity. These relationships give rise to time dilation, length contraction, and the twin “paradox” which we explain in detail. Throughout, we emphasize how these effects are tightly interwoven logically and graphically. Our graphical understanding leads to viewing space and time as a unified entity called spacetime whose geometry differs from that of space alone, giving rise to these remarkable effects. The same geometry gives rise to the energy?momentum relation that yields the famous equation E = mc2, which we explore in detail. We then show that this geometric model can explain gravity better than traditional models of the “force” of gravity. This gives rise to general relativity, which unites relativity and gravity in a coherent whole that spawns new insights into the dynamic nature of spacetime. We examine experimental tests and startling predictions of general relativity, from everyday applications (GPS) to exotic phenomena such as gravitomagnetism, gravitational waves, Big Bang cosmology, and especially black holes.
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48

Gelman, Andrew, and Deborah Nolan. Statistical graphics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0004.

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A statistical graph can offer an alternative compelling approach to teaching statistical thinking, but making good statistical graphs is hard to do. Each step in the process (e.g., change in scale, transform a variable, select colors, add a reference marker) engages students in better understanding data and models. However, this creative process is not easily encapsulated in a textbook. Since it is relatively easy to make a basic plot with statistical software, we can engage students in activities around making statistical graphs. This chapter provides guiding principles and lecture topics for teaching data visualization. The chapter contains exercises to deconstruct and reconstruct a plot, create a plot to reveal an important feature of the data, and turn a table of numbers into a plot.
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49

Hofmann, Heike. Graphical Tools For The Exploration Of Multivariate Categorical Data. Books On Demand, 2001.

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50

Curves and Surfaces for Computer Graphics. Springer, 2005.

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