Inference And Representation
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Inference And Representation
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Author : Mauricio Suárez
language : en
Publisher: University of Chicago Press
Release Date : 2024-01-11
Inference And Representation written by Mauricio Suárez and has been published by University of Chicago Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-11 with Science categories.
The first comprehensive defense of an inferential conception of scientific representation with applications to art and epistemology. Mauricio Suárez develops a conception of representation that delivers a compelling account of modeling practice. He begins by discussing the history and methodology of model building, charting the emergence of what he calls the modeling attitude, a nineteenth-century and fin de siècle development. Prominent cases of models, both historical and contemporary, are used as benchmarks for the accounts of representation considered throughout the book. After arguing against reductive naturalist theories of scientific representation, Suárez sets out his own account: a case for pluralism regarding the means of representation and minimalism regarding its constituents. He shows that scientists employ a variety of modeling relations in their representational practice—which helps them to assess the accuracy of their representations—while demonstrating that there is nothing metaphysically deep about the constituent relation that encompasses all these diverse means. The book also probes the broad implications of Suárez’s inferential conception outside scientific modeling itself, covering analogies with debates about artistic representation and philosophical thought over the past several decades.
Representation And Inference For Natural Language
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Author : Patrick Blackburn
language : en
Publisher: Center for the Study of Language and Information Publica Tion
Release Date : 2005
Representation And Inference For Natural Language written by Patrick Blackburn and has been published by Center for the Study of Language and Information Publica Tion this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computational linguistics categories.
How can computers distinguish the coherent from the unintelligible, recognize new information in a sentence, or draw inferences from a natural language passage? Computational semantics is an exciting new field that seeks answers to these questions, and this volume is the first textbook wholly devoted to this growing subdiscipline. The book explains the underlying theoretical issues and fundamental techniques for computing semantic representations for fragments of natural language. This volume will be an essential text for computer scientists, linguists, and anyone interested in the development of computational semantics.
Diagrammatic Representation And Inference
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Author : Ahti-Veikko Pietarinen
language : en
Publisher: Springer Nature
Release Date : 2020-08-17
Diagrammatic Representation And Inference written by Ahti-Veikko Pietarinen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-17 with Computers categories.
This book constitutes the refereed proceedings of the 11th International Conference on the Theory and Application of Diagrams, Diagrams 2020, held in Tallinn, Estonia, in August 2020.* The 20 full papers and 16 short papers presented together with 18 posters were carefully reviewed and selected from 82 submissions. The papers are organized in the following topical sections: diagrams in mathematics; diagram design, principles, and classification; reasoning with diagrams; Euler and Venn diagrams; empirical studies and cognition; logic and diagrams; and posters. *The conference was held virtually due to the COVID-19 pandemic. The chapters ‘Modality and Uncertainty in Data Visualization: A Corpus Approach to the Use of Connecting Lines,’ ‘On Effects of Changing Multi-Attribute Table Design on Decision Making: An Eye Tracking Study,’ ‘Truth Graph: A Novel Method for Minimizing Boolean Algebra Expressions by Using Graphs,’ ‘The DNA Framework of Visualization’ and ‘Visualizing Curricula’ are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Diagrammatic Representation And Inference
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Author : Amrita Basu
language : en
Publisher: Springer Nature
Release Date : 2021-09-21
Diagrammatic Representation And Inference written by Amrita Basu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-21 with Computers categories.
This book constitutes the refereed proceedings of the 12th International Conference on the Theory and Application of Diagrams, Diagrams 2021, held virtually in September 2021. The 16 full papers and 25 short papers presented together with 16 posters were carefully reviewed and selected from 94 submissions. The papers are organized in the following topical sections: design of concrete diagrams; theory of diagrams; diagrams and mathematics; diagrams and logic; new representation systems; analysis of diagrams; diagrams and computation; cognitive analysis; diagrams as structural tools; formal diagrams; and understanding thought processes. 10 chapters are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Pattern Theory
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Author : Ulf Grenander
language : en
Publisher: Oxford University Press
Release Date : 2007
Pattern Theory written by Ulf Grenander and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with Computers categories.
Pattern Theory provides a comprehensive and accessible overview of the modern challenges in signal, data, and pattern analysis in speech recognition, computational linguistics, image analysis and computer vision. Aimed at graduate students in biomedical engineering, mathematics, computer science, and electrical engineering with a good background in mathematics and probability, the text includes numerous exercises and an extensive bibliography. Additional resources including extended proofs, selected solutions and examples are available on a companion website. The book commences with a short overview of pattern theory and the basics of statistics and estimation theory. Chapters 3-6 discuss the role of representation of patterns via condition structure. Chapters 7 and 8 examine the second central component of pattern theory: groups of geometric transformation applied to the representation of geometric objects. Chapter 9 moves into probabilistic structures in the continuum, studying random processes and random fields indexed over subsets of Rn. Chapters 10 and 11 continue with transformations and patterns indexed over the continuum. Chapters 12-14 extend from the pure representations of shapes to the Bayes estimation of shapes and their parametric representation. Chapters 15 and 16 study the estimation of infinite dimensional shape in the newly emergent field of Computational Anatomy. Finally, Chapters 17 and 18 look at inference, exploring random sampling approaches for estimation of model order and parametric representing of shapes.
Representation And Understanding
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Author : Daniel Gureasko Bobrow
language : en
Publisher: Morgan Kaufmann
Release Date : 1975-09-28
Representation And Understanding written by Daniel Gureasko Bobrow and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1975-09-28 with Computers categories.
Theory of representation; New memory models; Higher level structures; Semantic knowledge in understander systems.
Pattern Theory
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Author : Ulf Grenander
language : en
Publisher: OUP Oxford
Release Date : 2006-12-14
Pattern Theory written by Ulf Grenander and has been published by OUP Oxford this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-12-14 with Technology & Engineering categories.
Pattern Theory: From Representation to Inference provides a comprehensive and accessible overview of the modern challenges in signal, data and pattern analysis in speech recognition, computational linguistics, image analysis and computer vision. Aimed at graduate students in biomedical engineering, mathematics, computer science and electrical engineering with a good background in mathematics and probability, the text includes numerous exercises and an extensive bibliography. Additional resources including extended proofs, selected solutions and examples are available on a companion website. The book commences with a short overview of pattern theory and the basics of statistics and estimation theory. Chapters 3-6 discuss the role of representation of patterns via conditioning structure and Chapters 7 and 8 examine the second central component of pattern theory: groups of geometric transformation applied to the representation of geometric objects. Chapter 9 moves into probabilistic structures in the continuum, studying random processes and random fields indexed over subsets of Rn, and Chapters 10, 11 continue with transformations and patterns indexed over the continuum. Chapters 12-14 extend from the pure representations of shapes to the Bayes estimation of shapes and their parametric representation. Chapters 15 and 16 study the estimation of infinite dimensional shape in the newly emergent field of Computational Anatomy, and finally Chapters 17 and 18 look at inference, exploring random sampling approaches for estimation of model order and parametric representing of shapes.
Diagrammatic Representation And Inference
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Author : Tim Dwyer
language : en
Publisher: Springer
Release Date : 2014-08-04
Diagrammatic Representation And Inference written by Tim Dwyer and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-04 with Computers categories.
This book constitutes the refereed proceedings of the 8th International Conference on the Theory and Application of Diagrams, Diagrams 2014, held in Melbourne, VIC, Australia in July/August 2014. The 15 revised full papers and 9 short papers presented together with 6 posters were carefully reviewed and selected from 40 submissions. The papers have been organized in the following topical sections: diagram layout, diagram notations, diagramming tools, diagrams in education, empirical studies and logic and diagrams.
Inference Method And Decision
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Author : R.D. Rosenkrantz
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Inference Method And Decision written by R.D. Rosenkrantz and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Science categories.
This book grew out of previously published papers of mine composed over a period of years; they have been reworked (sometimes beyond recognition) so as to form a reasonably coherent whole. Part One treats of informative inference. I argue (Chapter 2) that the traditional principle of induction in its clearest formulation (that laws are confirmed by their positive cases) is clearly false. Other formulations in terms of the 'uniformity of nature' or the 'resemblance of the future to the past' seem to me hopelessly unclear. From a Bayesian point of view, 'learning from experience' goes by conditionalization (Bayes' rule). The traditional stum bling block for Bayesians has been to fmd objective probability inputs to conditionalize upon. Subjective Bayesians allow any probability inputs that do not violate the usual axioms of probability. Many subjectivists grant that this liberality seems prodigal but own themselves unable to think of additional constraints that might plausibly be imposed. To be sure, if we could agree on the correct probabilistic representation of 'ignorance' (or absence of pertinent data), then all probabilities obtained by applying Bayes' rule to an 'informationless' prior would be objective. But familiar contra dictions, like the Bertrand paradox, are thought to vitiate all attempts to objectify 'ignorance'. BuUding on the earlier work of Sir Harold Jeffreys, E. T. Jaynes, and the more recent work ofG. E. P. Box and G. E. Tiao, I have elected to bite this bullet. In Chapter 3, I develop and defend an objectivist Bayesian approach.
Probabilistic Graphical Models
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Author : Luis Enrique Sucar
language : en
Publisher: Springer Nature
Release Date : 2020-12-23
Probabilistic Graphical Models written by Luis Enrique Sucar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-23 with Computers categories.
This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests possible course outlines for instructors This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference. Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.