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| Author | : Hans Rott |
| Publisher | : Clarendon Press |
| Total Pages | : 404 |
| Release | : 2001 |
| Genre | : Mathematics |
| ISBN | : 9780198503064 |
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.
| Author | : Kenneth P. Burnham |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 512 |
| Release | : 2007-05-28 |
| Genre | : Mathematics |
| ISBN | : 0387224564 |
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.
| Author | : Alexander Bochman |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 439 |
| Release | : 2013-03-14 |
| Genre | : Computers |
| ISBN | : 3662045605 |
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.
| Author | : Michel Regenwetter |
| Publisher | : Cambridge University Press |
| Total Pages | : 262 |
| Release | : 2006-05-15 |
| Genre | : Education |
| ISBN | : 9780521536660 |
Behavioral Social Choice looks at the probabilistic foundations of collective decision-making rules. The authors challenge much of the existing theoretical wisdom about social choice processes, and seek to restore faith in the possibility of democratic decision-making. In particular, they argue that worries about the supposed prevalence of majority rule cycles that would preclude groups from reaching a final decision about what alternative they prefer have been greatly overstated. In practice, majority rule can be expected to work well in most real-world settings. They provide new insights into how alternative model specifications can change our estimates of social orderings.
| Author | : Deborah G. Mayo |
| Publisher | : Cambridge University Press |
| Total Pages | : 503 |
| Release | : 2018-09-20 |
| Genre | : Mathematics |
| ISBN | : 1108563309 |
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.
| Author | : Eddo Rigotti |
| Publisher | : Springer |
| Total Pages | : 325 |
| Release | : 2018-12-10 |
| Genre | : Language Arts & Disciplines |
| ISBN | : 3030045684 |
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.
| Author | : David R. Anderson |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 203 |
| Release | : 2007-12-22 |
| Genre | : Science |
| ISBN | : 0387740759 |
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.
| Author | : Rupert G. Jr. Miller |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 311 |
| Release | : 2012-12-06 |
| Genre | : Mathematics |
| ISBN | : 1461381223 |
Simultaneous Statistical Inference, which was published originally in 1966 by McGraw-Hill Book Company, went out of print in 1973. Since then, it has been available from University Microfilms International in xerox form. With this new edition Springer-Verlag has republished the original edition along with my review article on multiple comparisons from the December 1977 issue of the Journal of the American Statistical Association. This review article covered developments in the field from 1966 through 1976. A few minor typographical errors in the original edition have been corrected in this new edition. A new table of critical points for the studentized maximum modulus is included in this second edition as an addendum. The original edition included the table by K. C. S. Pillai and K. V. Ramachandran, which was meager but the best available at the time. This edition contains the table published in Biometrika in 1971 by G. 1. Hahn and R. W. Hendrickson, which is far more comprehensive and therefore more useful. The typing was ably handled by Wanda Edminster for the review article and Karola Decleve for the changes for the second edition. My wife, Barbara, again cheerfully assisted in the proofreading. Fred Leone kindly granted permission from the American Statistical Association to reproduce my review article. Also, Gerald Hahn, Richard Hendrickson, and, for Biometrika, David Cox graciously granted permission to reproduce the new table of the studentized maximum modulus. The work in preparing the review article was partially supported by NIH Grant ROI GM21215.
| Author | : Alberto Maria Segre |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 521 |
| Release | : 2014-06-28 |
| Genre | : Computers |
| ISBN | : 1483297403 |
Machine Learning Proceedings 1989
| Author | : David J. C. MacKay |
| Publisher | : Cambridge University Press |
| Total Pages | : 694 |
| Release | : 2003-09-25 |
| Genre | : Computers |
| ISBN | : 9780521642989 |
Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.
