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Books on the topic 'Predictive Patterns'

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Published: 11 March 2023

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1

Leenaars, Antoon A. Suicide notes: Predictive clues and patterns. New York, N.Y: Human Sciences Press, 1988.

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2

G, Moore Donald, and Geological Survey (U.S.), eds. Predictive spatial modeling of narcotic crop growth patterns. Sioux Falls, S.D: U.S. Dept. of the Interior, U.S. Geological Survey, 1986.

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3

G, Moore Donald, and Geological Survey (U.S.), eds. Predictive spatial modeling of narcotic crop growth patterns. Sioux Falls, S.D: U.S. Dept. of the Interior, U.S. Geological Survey, 1986.

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4

Bartlett, Sheryl Anne. Predictive and posterior distributions for normal multivariate data with missing monotone patterns. Toronto: University of Toronto, Dept. of Statistics, 1985.

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5

G, Anderson David. Archaeology, history, and predictive modeling research at Fort Polk, 1972-2002. Tuscaloosa: University of Alabama Press, 2003.

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6

Dietrich, Daniel S. Predicting radiation characteristics from antenna physical dimensions. Monterey, Calif: Naval Postgraduate School, 1992.

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7

Tremblay, Pierre. Patterns in criminal achievement: Wilson and Abrahamse revisited. Montréal: Université de Montréal, 1999.

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8

1939-, Wyss Max, Shimazaki K. 1946-, and Ito Akihiko, eds. Seismicity patterns, their statistical significance and physical meaning. Basel: Birkhäuser Verlag, 1999.

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9

Lundholm, Steven E. Predicting antenna parameters from antenna physical dimensions. Monterey, Calif: Naval Postgraduate School, 1993.

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10

Pham, Duc Truong. Neural Networks for Identification, Prediction and Control. London: Springer London, 1995.

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11

Kil, David H. Pattern recognition and prediction with applications to signal characterization. Woodbury, N.Y: AIP Press, 1996.

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12

P, Vernier, Cumming Steven G, and Sustainable Forest Management Network (Canada), eds. Predicting landscape patterns from stand attribute data in the Alberta boreal mixedwood. Edmonton, Alta: Sustainable Forest Management Network, 1998.

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13

de, Nijs Ton. Modelling land use change: Improving the prediction of future land use patterns. Utrecht: Koninklijk Nederlands Aardrijkskundig Genootschap, 2009.

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14

Modelling land use change: Improving the prediction of future land use patterns. Utrecht: Koninklijk Nederlands Aardrijkskundig Genootschap, 2009.

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15

Gerry, Donald D. Mathcad computer applications predicting antenna parameters from antenna physical dimensions and ground characteristics. Monterey, Calif: Naval Postgraduate School, 1993.

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16

Barabási, Albert-László. Bursts: The hidden pattern behind everything we do. New York, N.Y: Dutton, 2010.

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17

McKenzie, Donald. Predicting the effect of fire on large-scale vegetation patterns in North America. [Portland, OR] (333 S.W. First Ave., P.O. Box 3890, Portland 97208-3890): U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 1996.

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18

McKenzie, Donald. Predicting the effect of fire on large-scale vegetation patterns in North America. Portland, Or. (333 S.W. First Ave., P.O. Box 3890, Portland 97208-3890): U.S. Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, 1996.

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19

Yang, C. Prediction of the pattern performance for the Aeroassist Flight Experiment (AFE) spacecraft. Hampton, Va: Langley Research Center, 1991.

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20

Richard, Somerville, Volfson Leonid B, and United States. National Aeronautics and Space Administration., eds. Pattern recognition of satellite cloud imagery for improved weather prediction: Final report. San Diego, Calif: Chase Consulting, Inc., 1987.

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21

R, Rudduck, Torres R, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Office., eds. Prediction of the pattern performance for the Aeroassist Flight Experiment (AFE) spacecraft. [Washington, DC]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1991.

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22

Poncet, Andreas. Design of adaptive models for system identification, signal prediction, and pattern classification. Konstanz: Hartung-Gorre, 1997.

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23

Bursts: The hidden pattern behind everything we do. New York, N.Y: Dutton, 2010.

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24

James, Harrison. The pattern & the prophecy: God's great code. Peterborough, Ont., Canada: Isaiah Publications, 1995.

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25

Willms, Jon Douglas. Patterns of academic achievement in public and private schools: Implications for public policy and future research. [Stanford, Calif: Institute for Research on Educational Finance and Governance, School of Education, Stanford University, 1985.

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26

Integrating Across Scales: Predicting Patterns of Change in Atlantic Salmon (1997 Braemar, Scotland). Selected proceedings of the international workshop entitled "Integrating across scales: predicting patterns of change in Atlantic Salmon" =: Sélection de comptes rendus de l'atelier international intitulé "Integrating across scales: predicting patterns of change in Atlantic Salmon", Braemar, Scotland, March 1997. Edited by Mather Martha Ellen, Parrish Donna L. 1953-, and Folt Carol L. [Ottawa, Ont.]: National Research Council of Canada, 1998.

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27

Grout, Marilyn A. Fracture history of the Plateau Creek and adjacent Colorado River valleys, southern Piceance Basin: Implications for predicting joint patterns at depth. [Reston, Va.?]: U.S. Dept. of the Interior, Geological Survey, 1985.

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28

Grout, Marilyn A. Fracture history of the Plateau Creek and adjacent Colorado River valleys, southern Piceance Basin: Implications for predicting joint patterns at depth. [Reston, Va.?]: U.S. Dept. of the Interior, Geological Survey, 1985.

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29

Grout, Marilyn A. Fracture history of the Plateau Creek and adjacent Colorado River valleys, southern Piceance Basin: Implications for predicting joint patterns at depth. [Reston, Va.?]: U.S. Dept. of the Interior, Geological Survey, 1985.

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30

John, Mason. Expressing generality. Milton Keynes: Centre for Mathematics Education, Open University, 1988.

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31

California Weather Symposium (1994 Sierra College). Predicting heavy rainfall events in California: A symposium to share weather pattern knowledge : Sierra College, Rocklin, California, June 25, 1994. Rocklin, Calif: Sierra College Science Center, 1994.

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32

Robinson, Grant. The demand for and production of a new technique for predicting expenditure patterns of construction projects for estate services directorate of the D.H.S.S. (Northern Ireland). [s.l: The Author], 1994.

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33

Julie, Coates, ed. Nine shift: Work, life and education in the 21st century. River Falls, Wis: LERN Books, 2004.

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34

Kiefer, Peter. Mobile intention recognition. New York: Springer, 2012.

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35

Predictive spatial modeling of narcotic crop growth patterns. Sioux Falls, S.D: U.S. Dept. of the Interior, U.S. Geological Survey, 1986.

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36

Reid, Basil Anthony. Developing GIS-based weights of evidence predictive model of pre-Columbian sites in Trinidad. 2003.

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37

WYSS, Max. Earthquake Prediction and Seismicity Patterns. Birkhauser, 2014.

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38

WYSS. Earthquake Prediction and Seismicity Patterns. Birkhauser Verlag, 2013.

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39

Llewellyn, Sue. What Do Dreams Do? Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198818953.001.0001.

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What is a dream? It’s a complex, non-obvious pattern derived from your experience. But you haven’t actually experienced it. Strange. Revealing complex, hidden patterns makes dreams odd. Dreams associate elements of different experiences to make something new: a pattern you didn’t know was there until you dreamt it. Patterns are discernible forms in the way something happens or is done. Some patterns are easy to spot, being certain and obvious: night follows day. Patterns in human/animal experiences are less obvious because, first, the patterned elements appear at different times or places and, second, the pattern exhibits tendencies not certainties. Spotting such patterns depends on non-obvious associations. If prompted with ‘sea’, while awake, your logical brain makes obvious associations, ‘beach’ or ‘boat’, with a seaside pattern i.e. beach-boat-seaside. But after awakening from dreaming, when your brain is still tuned to non-obvious associations, ‘sick’ may come to mind. A less obvious element of sea experiences. You tend to seasickness when it’s rough. But you also get sick if you eat shellfish, have a migraine, or travel in cars—but only if you read. Sea–rough–car–read–shellfish–migraine. Visualizing these non-obvious associations between elements of different experiences becomes dream-like. Dreaming brains evolved to identify non-obvious associations. Across evolutionary time, you didn’t want to get sick. Survival depended on being well enough to anticipate the non-obvious patterns of predators and human competitors, while securing access to food and water. Making associations drives many, if not all, brain functions. Dream associations support memory, emotional stability, creativity, unconscious decision-making, and prediction, while also contributing to mental illness. This book explains how.
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40

Human Activity Recognition and Prediction. Springer, 2016.

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41

Fu, Yun. Human Activity Recognition and Prediction. Springer, 2018.

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42

Fu, Yun. Human Activity Recognition and Prediction. Springer London, Limited, 2015.

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43

Brayne, Sarah. Predict and Surveil. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780190684099.001.0001.

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The scope of criminal justice surveillance, from policing to incarceration, has expanded rapidly in recent decades. At the same time, the use of big data has spread across a range of fields, including finance, politics, health, and marketing. While law enforcement’s use of big data is hotly contested, very little is known about how the police actually use it in daily operations and with what consequences. This book offers an inside look at how police use big data and new surveillance technologies, leveraging on-the-ground fieldwork with one of the most technologically advanced law enforcement agencies in the world—the Los Angeles Police Department. Drawing on original interviews and ethnographic observations from over two years of fieldwork with the LAPD, the text examines the causes and consequences of big data and algorithmic control. It reveals how the police use predictive analytics and new surveillance technologies to deploy resources, identify criminal suspects, and conduct investigations; how the adoption of big data analytics transforms police organizational practices; and how the police themselves respond to these new data-driven practices. While big data analytics has the potential to reduce bias, increase efficiency, and improve prediction accuracy, the book argues that it also reproduces and deepens existing patterns of inequality, threatens privacy, and challenges civil liberties.
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44

Miksza, Peter, and Kenneth Elpus. Structural Equation Modeling. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0014.

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This chapter presents structural equation modeling as a tool for conducting research regarding how collections of variables may be related to each other as well as to a particular outcome or even multiple outcomes. Structural equation modeling refers to a collection of analytical techniques that can be used to model complex patterns of predictive relationships among a collection of both measured and latent variables. As a statistical tool, structural equation modeling combines the features of regression and factor analysis. The chapter offers conceptual illustrations and practical steps for carrying out structural equation modeling by describing mediation and moderation analyses in the context of music education research.
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45

Mithun, Marianne. Polysynthesis in North America. Edited by Michael Fortescue, Marianne Mithun, and Nicholas Evans. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199683208.013.16.

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North America is generally regarded as an area rich in polysynthetic languages. Structures usually associated with the type are indeed pervasive, but the variety they show indicates that neither polysynthesis itself, nor any of the characteristics typically cited, is monolithic or criterial. Each property shows varying degrees of development and robustness, none of which is predictive of any of the others. Nevertheless, a survey of the languages shows that the characteristics do tend to co-occur, and furthermore that particular semantic and structural patterns of development can be seen in major linguistic areas, suggesting that language contact may play a role in shaping the type.
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46

Shimazaki, Kunihiko, Akihiko Ito, and Max Wyss. Seismicity Patterns, Their Statistical Significance and Physical Meaning. Birkhauser Verlag, 2012.

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47

Kagan, Jerome. Five Constraints on Predicting Behavior. The MIT Press, 2018. http://dx.doi.org/10.7551/mitpress/9780262036528.001.0001.

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Scientists were unable to study the relation of brain to mind until the invention of technologies that measured the brain activity accompanying psychological processes. Yet even with these new tools, conclusions are tentative or simply wrong. This book describes five conditions that place serious constraints on the ability to predict mental or behavioral outcomes based on brain data: the setting in which evidence is gathered, the expectations of the subject, the source of the evidence that supports the conclusion, the absence of studies that examine patterns of causes with patterns of measures, and the habit of borrowing terms from psychology. The book describes the importance of context, and how the experimental setting—including the room, the procedure, and the species, age, and sex of both subject and examiner—can influence the conclusions. It explains how subject expectations affect all brain measures; considers why brain and psychological data often yield different conclusions; argues for relations between patterns of causes and outcomes rather than correlating single variables; and criticizes the borrowing of psychological terms to describe brain evidence. Brain sites cannot be in a state of “fear.” A deeper understanding of the brain's contributions to behavior, the book argues, requires investigators to acknowledge these five constraints in the design or interpretation of an experiment.
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48

L, Taylor Charles. Seismicity patterns precursory to large Circum-Pacific earthquakes. 1988.

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49

Patterns of the Future: Understanding the Next Wave of Global Change. World Scientific Publishing Co Pte Ltd, 2017.

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50

Mithun, Marianne. Argument Marking in the Polysynthetic Verb and Its Implications. Edited by Michael Fortescue, Marianne Mithun, and Nicholas Evans. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199683208.013.4.

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It is generally agreed that the essence of polysynthesis goes beyond sheer numbers of morphemes per word, but which other properties might be criterial is unclear. Most frequently cited is the marking of core arguments within the verb, such that the key elements of the clause, predicate, and arguments, are contained within that one word. Also often cited are noun incorporation, applicatives, rich inventories of adverbial affixes, and pragmatically motivated word order. But argument marking on the verb is not categorical: pronominal affix paradigms show a range of differential marking patterns dependent on various semantic and grammatical features, none reliably predictive of other characteristics typically associated with polysynthesis. Yet these characteristics tend to cluster, indicating that they are not reflections of a single, underlying governing principle, but rather constitute structural complexes that emerge from constellations of cognitive and social factors favoring the development and maintenance of complex morphologies.
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