Foundations and Philosophy of Epistemic Applications of Probability Theory PDF Download
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Author: W.L. Harper
Publisher: Springer Science & Business Media
ISBN: 9789027706171
Category : Gardening
Languages : en
Pages : 334
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Book Description
Proceedings of an International Research Colloquium held at the University of Western Ontario, 10-13 May 1973.
Author: W.L. Harper
Publisher: Springer Science & Business Media
ISBN: 9789027706171
Category : Gardening
Languages : en
Pages : 334
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Book Description
Proceedings of an International Research Colloquium held at the University of Western Ontario, 10-13 May 1973.
Author: William Leonard Harper
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: William Leonard Harper
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 264
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Book Description
Author: William Leonard Harper
Publisher: Springer
ISBN: 9789027706140
Category : Science
Languages : en
Pages :
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Book Description
Author: W.L. Harper
Publisher: Springer
ISBN: 9789027706195
Category : Science
Languages : en
Pages : 456
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Book Description
In May of 1973 we organized an international research colloquium on foundations of probability, statistics, and statistical theories of science at the University of Western Ontario. During the past four decades there have been striking formal advances in our understanding of logic, semantics and algebraic structure in probabilistic and statistical theories. These advances, which include the development of the relations between semantics and metamathematics, between logics and algebras and the algebraic-geometrical foundations of statistical theories (especially in the sciences), have led to striking new insights into the formal and conceptual structure of probability and statistical theory and their scientific applications in the form of scientific theory. The foundations of statistics are in a state of profound conflict. Fisher's objections to some aspects of Neyman-Pearson statistics have long been well known. More recently the emergence of Bayesian statistics as a radical alternative to standard views has made the conflict especially acute. In recent years the response of many practising statisticians to the conflict has been an eclectic approach to statistical inference. Many good statisticians have developed a kind of wisdom which enables them to know which problems are most appropriately handled by each of the methods available. The search for principles which would explain why each of the methods works where it does and fails where it does offers a fruitful approach to the controversy over foundations.
Author: William Leonard Harper
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Jianqing Fan
Publisher: CRC Press
ISBN: 0429527616
Category : Mathematics
Languages : en
Pages : 974
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Book Description
Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.
Author: W.L. Harper
Publisher: Springer Science & Business Media
ISBN: 9400991177
Category : Science
Languages : en
Pages : 345
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Book Description
With publication of the present volume, The University of Western Ontario Series in Philosophy of Science enters its second phase. The first fourteen volumes in the Series were produced under the managing editorship of Professor James J. Leach, with the cooperation of a local editorial board. Many of these volumes resulted from colloguia and workshops held in con nection with the University of Western Ontario Graduate Programme in Philosophy of Science. Throughout its seven year history, the Series has been devoted to publication of high quality work in philosophy of science con sidered in its widest extent, including work in philosophy of the special sciences and history of the conceptual development of science. In future, this general editorial emphasis will be maintained, and hopefully, broadened to include important works by scholars working outside the local context. Appointment of a new managing editor, together with an expanded editorial board, brings with it the hope of an enlarged international presence for the Series. Serving the publication needs of those working in the various subfields within philosophy of science is a many-faceted operation. Thus in future the Series will continue to produce edited proceedings of worthwhile scholarly meetings and edited collections of seminal background papers. How ever, the publication priorities will shift emphasis to favour production of monographs in the various fields covered by the scope of the Series. THE MANAGING EDITOR vii W. L. Harper, R. Stalnaker, and G. Pearce (eds.), lIs, vii.
Author: Aris Spanos
Publisher: Cambridge University Press
ISBN: 1107185149
Category : Business & Economics
Languages : en
Pages : 787
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Book Description
This empirical research methods course enables informed implementation of statistical procedures, giving rise to trustworthy evidence.
Author: Deborah G. Mayo
Publisher: Cambridge University Press
ISBN: 1108563309
Category : Mathematics
Languages : en
Pages : 503
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Book Description
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.
