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Books on the topic 'Relationship inference'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Dippery, Kevin L. Changeover inference: Estimating the relationship between DT and OT data '. Naval Postgraduate School, 1997.

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2

Jonathan, Wood. Counsellor reliance on non-verbal sources of inference and its relationship to counsellor orientation. University of Surrey, 1994.

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3

Varlamov, Oleg. Mivar databases and rules. 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 th

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4

Statistical Inference and Statistical Relationship (Statistical Inference & Statistical Relationship). 4th ed. Hafner Press, 1986.

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5

Relationship Inference with Familias and R. Elsevier, 2016. http://dx.doi.org/10.1016/c2014-0-01828-x.

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6

Kendall's Advanced Theory of Statistics Vol. 2: Classical Inference and Relationship. Hodder Education Group, 1991.

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7

Egeland, Thore, Daniel Kling, and Petter Mostad. Relationship Inference with Familias and R: Statistical Methods in Forensic Genetics. Elsevier Science & Technology Books, 2015.

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8

Egeland, Thore, Daniel Kling, and Petter Mostad. Relationship Inference with Familias and R: Statistical Methods in Forensic Genetics. Elsevier Science & Technology Books, 2015.

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9

Golan, Amos. Info-Metrics and Statistical Inference. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199349524.003.0012.

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This chapter is the first of a two-chapter sequence looking into the relationship between info-metrics and the more familiar statistical methods of inference, with an emphasis on information-theoretic methods. In this chapter I concentrate on discrete models. The relationship between info-metrics and information-theoretic statistical methods is established via duality theory, which provides a way for specifying all inferential methods as constrained optimization models. Since the objective here is to compare different approaches and philosophies, the analysis and examples are kept simple. A ma

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10

Stuart, Alan, and J. Keith Ord. Kendall's Advanced Theory of Statistics Volume 2 5ed Classical Inference and Relationship (Oup Edition). Hodder & Stoughton Educational Division, 1991.

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11

Kendall, Maurice G. Kendall's Advanced Theory of Statistics: Classical Inference and Relationship (Kendall, Maurice George//Kendall's Advanced Theory of Statistics 6th ed). 6th ed. John Wiley & Sons Inc, 1997.

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12

Stuart, Alan, Maurice Kendall, and J. Keith Ord. Kendall's Advanced Theory of Statistics: Volume 2: Classical Inference and Relationship (Kendall, Maurice George//Kendall's Advanced Theory of Statistics 6th ed). A Charles Griffin Book, 1991.

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13

M, Amabile Teresa, Singer Jill, Chedd-Angier Production Company, Consortium for Mathematics and Its Applications (U.S.)., and Magic Lantern Communications, eds. Inference for relationships [videorecording]. 1989.

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14

Holyoak, Keith J., and Hee Seung Lee. Inferring Causal Relations by Analogy. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.25.

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When two situations share a common pattern of relationships among their constituent elements, people often draw an analogy between a familiar source analog and a novel target analog. This chapter reviews major subprocesses of analogical reasoning and discusses how analogical inference is guided by causal relations. Psychological evidence suggests that analogical inference often involves constructing and then running a causal model. It also provides some examples of analogies and models that have been used as tools in science education to foster understanding of critical causal relations. A Bay

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15

Griffiths, Thomas L. Formalizing Prior Knowledge in Causal Induction. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.38.

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Prior knowledge plays a central role in causal induction, helping to explain how people are capable of identifying causal relationships from small amounts of data. Bayesian inference provides a way to characterize the influence that prior knowledge should have on causal induction, as well as an explanation for how that knowledge could itself be acquired. Using the theory-based causal induction framework of Griffiths and Tenenbaum (2009), this chapter reviews recent work exploring the relationship between prior knowledge and causal induction, highlighting some of the ways in which people’s expe

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16

Desoete, Annemie. Cognitive Predictors of Mathematical Abilities and Disabilities. Edited by Roi Cohen Kadosh and Ann Dowker. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199642342.013.033.

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The cognitive predictors of mathematical abilities and disabilities/disorders (MD) were investigated. An overview is given of the prediction by early numeracy skills such as Piagetian logical thinking, counting, and number comparison skills. In addition, studies of relationships between language and numeracy in kindergarten and grade 1 are discussed. Moreover, the chapter sought out to extend our knowledge regarding the relationship between motor, visual and visuomotor skills and mathematical abilities and disabilities. Furthermore, the chapter discusses studies of working memory, inhibition,

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17

Soloveitchik, Haym. Collected Essays. Liverpool University Press, 2020. http://dx.doi.org/10.3828/liverpool/9781904113997.001.0001.

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Continuing the contribution to medieval Jewish intellectual history, this book's author focuses here on the radical pietist movement of Ḥasidei Ashkenaz and its main literary work, Sefer Ḥasidim, and on the writings and personality of the Provençal commentator Ravad of Posquières. In both areas the author challenges mainstream views to provide a new understanding of medieval Jewish thought. Some of the essays are revised and updated versions of work previously published, and some are entirely new, but in all of them the author challenges reigning views to provide a new understanding of medieva

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18

Neapolitan, Richard, and Xia Jiang. The Bayesian Network Story. Edited by Alan Hájek and Christopher Hitchcock. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199607617.013.31.

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Bayesian networks are now among the leading architectures for reasoning with uncertainty in artificial intelligence. This chapter concerns their story, namely what they are, how and why they came into being, how we obtain them, and what they actually represent. First, it is shown that a standard application of Bayes’ Theorem constitutes inference in a two-node Bayesian network. Then more complex Bayesian networks are presented. Next the genesis of Bayesian networks and their relationship to causality is presented. A technique for learning Bayesian networks from data follows. Finally, a discuss

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19

Lombrozo, Tania, and Nadya Vasilyeva. Causal Explanation. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.22.

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Explanation and causation are intimately related. Explanations often appeal to causes, and causal claims are often answers to implicit or explicit questions about why or how something occurred. This chapter considers what we can learn about causal reasoning from research on explanation. In particular, it reviews an emerging body of work suggesting that explanatory considerations—such as the simplicity or scope of a causal hypothesis—can systematically influence causal inference and learning. It also discusses proposed distinctions among types of explanations and reviews the effects of each exp

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20

Lipton, Peter. Causation and Explanation. Edited by Helen Beebee, Christopher Hitchcock, and Peter Menzies. Oxford University Press, 2010. http://dx.doi.org/10.1093/oxfordhb/9780199279739.003.0030.

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In its simplest form, a causal model of explanation maintains that to explain some phenomenon is to give some information about its causes. This prompts four questions that will structure the discussion to follow. The first is whether all explanations are causal. The second is whether all causes are explanatory. The answer to both of these questions turns out to be negative, and seeing why this is so helps to clarify the relationship between causation and explanation. The third question is itself a request for an explanation: Why do causes explain, when they do? Why, for example, do causes exp

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21

Pollock, John L. Nomic Probability and the Foundations of Induction. Oxford University Press, 1990. http://dx.doi.org/10.1093/oso/9780195060133.001.0001.

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In this book Pollock deals with the subject of probabilistic reasoning, making general philosophical sense of objective probabilities and exploring their relationship to the problem of induction. He argues that probability is fundamental not only to physical science, but to induction, epistemology, the philosophy of science and much of the reasoning relevant to artificial intelligence. Pollock's main claim is that the fundamental notion of probability is nomic--that is, it involves the notion of natural law, valid across possible worlds. The various epistemic and statistical conceptions of pro

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22

Li, Nan, Natalie Jomini Stroud, and Kathleen Hall Jamieson. Overcoming False Causal Attribution. Edited by Kathleen Hall Jamieson, Dan M. Kahan, and Dietram A. Scheufele. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190497620.013.46.

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In a study published in 1998 in The Lancet, British researchers Wakefield and colleagues described an association between the measles, mumps, and rubella (MMR) vaccine and the onset of autism. Although the MMR–autism association failed to replicate and the lead author was discredited, the purported relationship decreased public confidence in vaccine safety. Parents continue to cite the MMR controversy as a factor complicating their decisions about vaccinating their children. This chapter focuses on misinformation involving false causality and discusses how it might exert persistent influence o

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23

Deppermann, Arnulf, and Michael Haugh, eds. Action Ascription in Interaction. Cambridge University Press, 2022. http://dx.doi.org/10.1017/9781108673419.

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Bringing together a team of global experts, this is the first volume to focus on the ways in which meanings are ascribed to actions in social interaction. It builds on the research traditions of Conversation Analysis and Pragmatics, and highlights the role of interactional, social, linguistic, multimodal, and epistemic factors in the formation and ascription of action-meanings. It shows how inference and intention ascription are displayed and drawn upon by participants in social interaction. Each chapter reveals practices, processes, and uses of action ascription, based on the analysis of audi

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24

Han, Shihui. The Sociocultural Brain. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198743194.001.0001.

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Is the human brain shaped by our sociocultural experiences, and if so, how? What are the neural correlates of cultural diversity of human behavior? Do genes interact with sociocultural experiences to moderate human brain functional organization and behavior? The Sociocultural Brain examines the relationship between human sociocultural experience and brain functional organization. It introduces brain imaging studies that identify neural correlates of culturally familiar gesture, music, brand, and more. It reviews cultural neuroscience findings of cross-cultural differences in human brain activi

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25

Schmidt, Nicole M., Quynh C. Nguyen, and Theresa L. Osypuk. Experimental and Quasi-Experimental Designs in Neighborhood Health Effects Research. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190843496.003.0006.

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This chapter primarily discusses experiments and quasi-experiments (also known as natural experiments) as designs for examining the causal effect of neighborhood environments on health. The first half of this chapter discusses causal inference, and experimental, quasi-experimental, and longitudinal study designs. These designs are important for causal inference, by providing a clear temporal ordering and the ability to take into account (potential) time lags between exposure to neighborhood environment characteristics and changes in health and health behavior. The second half of the chapter di

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26

Miller, Alexander. Wittgenstein and the Possibility of Meaning. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/9780191946882.001.0001.

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Abstract Inspired by Ludwig Wittgenstein’s later philosophy, this book develops a new, non-reductionist, response to the sceptical argument about meaning famously developed in Saul Kripke’s book Wittgenstein on Rules and Private Language. It begins by outlining an intuitive notion of following a rule, explaining its relationship to the notions of linguistic meaning and intentional content. It then gives an outline and development of Kripke’s Wittgenstein’s sceptical argument, going into detail on the arguments against reductive dispositional accounts of meaning. It also explains Kripke’s Wittg

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27

Hitchcock, Christopher. Causal Modelling. Edited by Helen Beebee, Christopher Hitchcock, and Peter Menzies. Oxford University Press, 2010. http://dx.doi.org/10.1093/oxfordhb/9780199279739.003.0015.

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‘Causal modelling’ is a general term that applies to a wide variety of formal methods for representing, and facilitating inferences about, causal relationships. The end of the twentieth century saw an explosion of work on causal modelling, with contributions from such fields as statistics, computer science, and philosophy; as well as from more subject-specific disciplines such as econometrics and epidemiology. This article focuses on two programmes that have attracted considerable philosophical attention, one due to the computer scientist Judea Pearl and his collaborators, and the other to the

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28

Schadt, Eric E. Network Methods for Elucidating the Complexity of Common Human Diseases. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0002.

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The life sciences are now a significant contributor to the ever expanding digital universe of data, and stand poised to lead in both the generation of big data and the realization of dramatic benefit from it. We can now score variations in DNA across whole genomes; RNA levels and alternative isoforms, metabolite levels, protein levels, and protein state information across the transcriptome, metabolome and proteome; methylation status across the methylome; and construct extensive protein–protein and protein–DNA interaction maps, all in a comprehensive fashion and at the scale of populations of

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29

Marko, Peter B., and Michael W. Hart, eds. Genetic Analysis of Larval Dispersal, Gene Flow, and Connectivity. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786962.003.0012.

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Does the dispersal of planktonic larvae promote strong connections between marine populations? Here we describe some of the most commonly used population- and individual-based genetic methods that have enhanced our understanding of larval dispersal and marine connectivity. Both approaches have strengths and weaknesses. Choosing between them depends on whether researchers want to know about average effective rates of connectivity over long timescales (over hundreds to thousands of generations) or recent patterns of connectivity on shorter timescales (one to two generations). The use of both app

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30

Muentener, Paul, and Elizabeth Bonawitz. The Development of Causal Reasoning. Edited by Michael R. Waldmann. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199399550.013.40.

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Research on the development of causal reasoning has broadly focused on accomplishing two goals: understanding the origins of causal reasoning, and examining how causal reasoning changes with development. This chapter reviews evidence and theory that aim to fulfill both of these objectives. In the first section, it focuses on the research that explores the possible precedents for recognizing causal events in the world, reviewing evidence for three distinct mechanisms in early causal reasoning: physical launching events, agents and their actions, and covariation information. The second portion o

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31

Raposa, Michael L. Theosemiotic. Fordham University Press, 2020. http://dx.doi.org/10.5422/fordham/9780823289516.001.0001.

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This book is an attempt to adapt some of Peirce’s ideas, particularly his theory of semiotic, for the purpose of re-thinking certain issues in contemporary philosophical theology and the philosophy of religion. It begins with an historical sketch that links Peirce’s thought to that of earlier figures, certain contemporaries, and later thinkers and developments. Drawing on Peirce’s thought, the book then develops a semiotic conception of persons/selves and of community. It analyzes in some detail the role that acts of attention play in shaping human inferences and perception, while also explori

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32

Ray, Sumantra (Shumone), Sue Fitzpatrick, Rajna Golubic, Susan Fisher, and Sarah Gibbings, eds. Navigating research methods: basic concepts in biostatistics and epidemiology. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199608478.003.0002.

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This chapter provides an overview of the basic concepts in biostatistics and epidemiology. Section 1: Basic concepts in biostatistics The concepts in biostatistics include: 1. descriptive statistical methods (which comprise of frequency distribution, distribution shapes, and measures of central tendency and dispersion); and 2. inferential statistics which is applied to make inferences about a population from the sample data. Non-probability and probability sampling methods are outlined. This section provides simple explanation of the complex concepts of significance tests and confidence interv

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