Change, Choice and Inference

Change, Choice and Inference PDF

Author: Hans Rott

Publisher: Clarendon Press

Published: 2001

Total Pages: 404

ISBN-13: 9780198503064

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This work develops logical theories necessary to understand adaptable human reasoning & the design ofintelligent systems. It unifies lively & significant strands of research in logic, philosophy, economics & artificial intelligence.

Model Selection and Multimodel Inference

Model Selection and Multimodel Inference PDF

Author: Kenneth P. Burnham

Publisher: Springer Science & Business Media

Published: 2007-05-28

Total Pages: 512

ISBN-13: 0387224564

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A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. It contains several new approaches to estimating model selection uncertainty and incorporating selection uncertainty into estimates of precision. An array of examples is given to illustrate various technical issues. The text has been written for biologists and statisticians using models for making inferences from empirical data.

A Logical Theory of Nonmonotonic Inference and Belief Change

A Logical Theory of Nonmonotonic Inference and Belief Change PDF

Author: Alexander Bochman

Publisher: Springer Science & Business Media

Published: 2013-03-14

Total Pages: 439

ISBN-13: 3662045605

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This is the first book that integrates nonmonotonic reasoning and belief change into a single framework from an artificial intelligence logic point-of-view. The approach to both these subjects is based on a powerful notion of an epistemic state that subsumes both existing models for nonmonotonic inference and current models for belief change. Many results and constructions in the book are completely new and have not appeared earlier in the literature.

Logic and the Foundations of Game and Decision Theory - LOFT 8

Logic and the Foundations of Game and Decision Theory - LOFT 8 PDF

Author: Giacomo Bonanno

Publisher: Springer Science & Business Media

Published: 2010-08-25

Total Pages: 219

ISBN-13: 3642151639

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This book constitutes the refereed proceedings of the 8th International Conference on Logic and the Foundations of the Theory of Game and Decision Theory, LOFT8 2008, held in Amsterdam, The Netherlands, July 2008. This volume is based on a selection of the presented papers and invited talks. They survived a thorough and lengthy reviewing process. The LOFT conferences are interdisciplinary events that bring together researchers from a variety of fields: computer science, economics, game theory, linguistics, logic, multi-agent systems, psychology, philosophy, social choice and statistics. Its focus is on the general issue of rationality and agency. The papers collected in this volume reflect the contemporary interests and interdisciplinary scope of the LOFT conferences.

Inference in Argumentation

Inference in Argumentation PDF

Author: Eddo Rigotti

Publisher: Springer

Published: 2018-12-10

Total Pages: 325

ISBN-13: 3030045684

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This book investigates the role of inference in argumentation, considering how arguments support standpoints on the basis of different loci. The authors propose and illustrate a model for the analysis of the standpoint-argument connection, called Argumentum Model of Topics (AMT). A prominent feature of the AMT is that it distinguishes, within each and every single argumentation, between an inferential-procedural component, on which the reasoning process is based; and a material-contextual component, which anchors the argument in the interlocutors’ cultural and factual common ground. The AMT explains how these components differ and how they are intertwined within each single argument. This model is introduced in Part II of the book, following a careful reconstruction of the enormously rich tradition of studies on inference in argumentation, from the antiquity to contemporary authors, without neglecting medieval and post-medieval contributions. The AMT is a contemporary model grounded in a dialogue with such tradition, whose crucial aspects are illuminated in this book.

Long Way Down

Long Way Down PDF

Author: Jason Reynolds

Publisher: Simon and Schuster

Published: 2017-10-24

Total Pages: 319

ISBN-13: 1481438271

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“An intense snapshot of the chain reaction caused by pulling a trigger.” —Booklist (starred review) “Astonishing.” —Kirkus Reviews (starred review) “A tour de force.” —Publishers Weekly (starred review) A Newbery Honor Book A Coretta Scott King Honor Book A Printz Honor Book A Time Best YA Book of All Time (2021) A Los Angeles Times Book Prize Winner for Young Adult Literature Longlisted for the National Book Award for Young People’s Literature Winner of the Walter Dean Myers Award An Edgar Award Winner for Best Young Adult Fiction Parents’ Choice Gold Award Winner An Entertainment Weekly Best YA Book of 2017 A Vulture Best YA Book of 2017 A Buzzfeed Best YA Book of 2017 An ode to Put the Damn Guns Down, this is New York Times bestselling author Jason Reynolds’s electrifying novel that takes place in sixty potent seconds—the time it takes a kid to decide whether or not he’s going to murder the guy who killed his brother. A cannon. A strap. A piece. A biscuit. A burner. A heater. A chopper. A gat. A hammer A tool for RULE Or, you can call it a gun. That’s what fifteen-year-old Will has shoved in the back waistband of his jeans. See, his brother Shawn was just murdered. And Will knows the rules. No crying. No snitching. Revenge. That’s where Will’s now heading, with that gun shoved in the back waistband of his jeans, the gun that was his brother’s gun. He gets on the elevator, seventh floor, stoked. He knows who he’s after. Or does he? As the elevator stops on the sixth floor, on comes Buck. Buck, Will finds out, is who gave Shawn the gun before Will took the gun. Buck tells Will to check that the gun is even loaded. And that’s when Will sees that one bullet is missing. And the only one who could have fired Shawn’s gun was Shawn. Huh. Will didn’t know that Shawn had ever actually USED his gun. Bigger huh. BUCK IS DEAD. But Buck’s in the elevator? Just as Will’s trying to think this through, the door to the next floor opens. A teenage girl gets on, waves away the smoke from Dead Buck’s cigarette. Will doesn’t know her, but she knew him. Knew. When they were eight. And stray bullets had cut through the playground, and Will had tried to cover her, but she was hit anyway, and so what she wants to know, on that fifth floor elevator stop, is, what if Will, Will with the gun shoved in the back waistband of his jeans, MISSES. And so it goes, the whole long way down, as the elevator stops on each floor, and at each stop someone connected to his brother gets on to give Will a piece to a bigger story than the one he thinks he knows. A story that might never know an END…if Will gets off that elevator. Told in short, fierce staccato narrative verse, Long Way Down is a fast and furious, dazzlingly brilliant look at teenage gun violence, as could only be told by Jason Reynolds.

Statistical Inference as Severe Testing

Statistical Inference as Severe Testing PDF

Author: Deborah G. Mayo

Publisher: Cambridge University Press

Published: 2018-09-20

Total Pages: 503

ISBN-13: 1108563309

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Mounting failures of replication in social and biological sciences give a new urgency to critically appraising proposed reforms. This book pulls back the cover on disagreements between experts charged with restoring integrity to science. It denies two pervasive views of the role of probability in inference: to assign degrees of belief, and to control error rates in a long run. If statistical consumers are unaware of assumptions behind rival evidence reforms, they can't scrutinize the consequences that affect them (in personalized medicine, psychology, etc.). The book sets sail with a simple tool: if little has been done to rule out flaws in inferring a claim, then it has not passed a severe test. Many methods advocated by data experts do not stand up to severe scrutiny and are in tension with successful strategies for blocking or accounting for cherry picking and selective reporting. Through a series of excursions and exhibits, the philosophy and history of inductive inference come alive. Philosophical tools are put to work to solve problems about science and pseudoscience, induction and falsification.

Model Based Inference in the Life Sciences

Model Based Inference in the Life Sciences PDF

Author: David R. Anderson

Publisher: Springer Science & Business Media

Published: 2007-12-22

Total Pages: 203

ISBN-13: 0387740759

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This textbook introduces a science philosophy called "information theoretic" based on Kullback-Leibler information theory. It focuses on a science philosophy based on "multiple working hypotheses" and statistical models to represent them. The text is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals. Readers are however expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes reasonable competence in modeling and parameter estimation.

Knowledge Science, Engineering and Management

Knowledge Science, Engineering and Management PDF

Author: Songmao Zhang

Publisher: Springer

Published: 2015-10-23

Total Pages: 858

ISBN-13: 3319251597

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This book constitutes the refereed proceedings of the 8th International Conference on Knowledge Science, Engineering and Management, KSEM 2015, held in Chongqing, China, in October 2015. The 57 revised full papers presented together with 22 short papers and 5 keynotes were carefully selected and reviewed from 247 submissions. The papers are organized in topical sections on formal reasoning and ontologies; knowledge management and concept analysis; knowledge discovery and recognition methods; text mining and analysis; recommendation algorithms and systems; machine learning algorithms; detection methods and analysis; classification and clustering; mobile data analytics and knowledge management; bioinformatics and computational biology; and evidence theory and its application.