Search results for: inference

Parametric Statistical Inference

Author : James K. Lindsey
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Inference involves drawing conclusions about some general phenomenon from limited empirical observations in the face of random variability. Two central unifying components of statistics are the likelihood function and the exponential family. These are here brought together for the firsttime as the central themes of a book on statistical inference. This book is appropriate as an advanced undergraduate or graduate text in mathematical statistics.

Bayesian Inference in Statistical Analysis

Author : George E. P. Box
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Its main objective is to examine the application and relevance of Bayes' theorem to problems that arise in scientific investigation in which inferences must be made regarding parameter values about which little is known a priori. Begins with a discussion of some important general aspects of the Bayesian approach such as the choice of prior distribution, particularly noninformative prior distribution, the problem of nuisance parameters and the role of sufficient statistics, followed by many standard problems concerned with the comparison of location and scale parameters. The main thrust is an investigation of questions with appropriate analysis of mathematical results which are illustrated with numerical examples, providing evidence of the value of the Bayesian approach.

Probability Theory and Statistical Inference

Author : Aris Spanos
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A major textbook for students taking introductory courses in probability theory and statistical inference.

Inference to the Best Explanation

Author : Peter Lipton
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This publication is an unrivalled exposition of a theory of particular interest to students both of epistemology and the philosophy of science.

Ecological Inference

Author : Gary King
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Drawing upon the explosion of research in the field, a diverse group of scholars surveys strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays, first published in 2004, offers many important contributions to the study of ecological inference.

Essentials of Statistical Inference

Author : G. A. Young
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Concise account of main approaches; first textbook to synthesize modern computation with basic theory.

Statistical Inference Based on the likelihood

Author : Adelchi Azzalini
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The Likelihood plays a key role in both introducing general notions of statistical theory, and in developing specific methods. This book introduces likelihood-based statistical theory and related methods from a classical viewpoint, and demonstrates how the main body of currently used statistical techniques can be generated from a few key concepts, in particular the likelihood. Focusing on those methods, which have both a solid theoretical background and practical relevance, the author gives formal justification of the methods used and provides numerical examples with real data.

Grammatical Inference Learning Syntax from Sentences

Author : Laurent Miclet
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This book constitutes the refereed proceedings of the Third International Colloquium on Grammatical Inference, ICGI-96, held in Montpellier, France, in September 1996. The 25 revised full papers contained in the book together with two invited key papers by Magerman and Knuutila were carefully selected for presentation at the conference. The papers are organized in sections on algebraic methods and algorithms, natural language and pattern recognition, inference and stochastic models, incremental methods and inductive logic programming, and operational issues.

Statistical Inference

Author : Paul H. Garthwaite
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Statistical inference is the foundation on which much of statistical practice is built. This book covers the topic at a level suitable for students and professionals who need to understand these foundations.

Inference on the Low Level

Author : Hannes Leitgeb
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In contrast to the prevailing tradition in epistemology, the focus in this book is on low-level inferences, i.e., those inferences that we are usually not consciously aware of and that we share with the cat nearby which infers that the bird which she sees picking grains from the dirt, is able to fly. Presumably, such inferences are not generated by explicit logical reasoning, but logical methods can be used to describe and analyze such inferences. Part 1 gives a purely system-theoretic explication of belief and inference. Part 2 adds a reliabilist theory of justification for inference, with a qualitative notion of reliability being employed. Part 3 recalls and extends various systems of deductive and nonmonotonic logic and thereby explains the semantics of absolute and high reliability. In Part 4 it is proven that qualitative neural networks are able to draw justified deductive and nonmonotonic inferences on the basis of distributed representations. This is derived from a soundness/completeness theorem with regard to cognitive semantics of nonmonotonic reasoning. The appendix extends the theory both logically and ontologically, and relates it to A. Goldman's reliability account of justified belief.

Argument Inference and Dialectic

Author : R.C. Pinto
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This volume contains 12 papers addressed to researchers and advanced students in informal logic and related fields, such as argumentation, formal logic, and communications. Among the issues discussed are attempts to rethink the nature of argument and of inference, the role of dialectical context, and the standards for evaluating inferences, and to shed light on the interfaces between informal logic and argumentation theory, rhetoric, formal logic and cognitive psychology.

Nondeductive Inference

Author : Robert Ackermann
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Originally published in 1966. An introduction to current studies of kinds of inference in which validity cannot be determined by ordinary deductive models. In particular, inductive inference, predictive inference, statistical inference, and decision making are examined in some detail. The last chapter discusses the relationship of these forms of inference to philosophical notions of rationality. Special features of the monograph include a discussion of the legitimacy of various criteria for successful predictive inference, the development of an intuitive model which exhibits the difficulties of choosing probability measures over infinite sets, and a comparison of rival views on the foundations of probability in terms of the amount of information which the members of these schools believe suitable for fruitful formalization. The bibliographies include articles by statisticians accessible to students of symbolic logic.

Logic of Statistical Inference

Author : Emeritus University Professor Ian Hacking
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This book is a philosophical study of the basic principles of statistical reasoning. Professor Hacking has sought to discover the simple principles which underlie modern work in mathematical statistics and to test them, both at a philosophical level and in terms of their practical consequences fort statisticians. The ideas of modern logic are used to analyse these principles, and results are presented without the use of unfamiliar symbolism. It begins with a philosophical analysis of a few central concepts and then, using an elementary system of logic, develops most of the standard statistical theory. the analysis provides answers to many disputed questions about how to test statistical hypotheses and about how to estimate quantities in the light of statistical data. One product of the analysis is a sound and consistent rationale for R. A. Fisher's controversial concept of 'fiducial probability'.

Aspects of Statistical Inference

Author : A. H. Welsh
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Relevant, concrete, and thorough—the essential data-basedtext on statistical inference The ability to formulate abstract concepts and draw conclusionsfrom data is fundamental to mastering statistics. Aspects ofStatistical Inference equips advanced undergraduate and graduatestudents with a comprehensive grounding in statistical inference,including nonstandard topics such as robustness, randomization, andfinite population inference. A. H. Welsh goes beyond the standard texts and expertlysynthesizes broad, critical theory with concrete data and relevanttopics. The text follows a historical framework, uses real-datasets and statistical graphics, and treats multiparameter problems,yet is ultimately about the concepts themselves. Written with clarity and depth, Aspects of StatisticalInference: Provides a theoretical and historical grounding in statisticalinference that considers Bayesian, fiducial, likelihood, andfrequentist approaches Illustrates methods with real-data sets on diabeticretinopathy, the pharmacological effects of caffeine, stellarvelocity, and industrial experiments Considers multiparameter problems Develops large sample approximations and shows how to usethem Presents the philosophy and application of robustnesstheory Highlights the central role of randomization in statistics Uses simple proofs to illuminate foundational concepts Contains an appendix of useful facts concerning expansions,matrices, integrals, and distribution theory Here is the ultimate data-based text for comparing andpresenting the latest approaches to statistical inference.

Statistical Inference and Simulation for Spatial Point Processes

Author : Jesper Moller
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Spatial point processes play a fundamental role in spatial statistics and today they are an active area of research with many new applications. Although other published works address different aspects of spatial point processes, most of the classical literature deals only with nonparametric methods, and a thorough treatment of the theory and applications of simulation-based inference is difficult to find. Written by researchers at the top of the field, this book collects and unifies recent theoretical advances and examples of applications. The authors examine Markov chain Monte Carlo algorithms and explore one of the most important recent developments in MCMC: perfect simulation procedures.

Diagrammatic Representation and Inference

Author : Ashok K Goel
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The 6th International Conference on the Theory and Application of Diagrams – Diagrams 2010 – was held in Portland, USA in August 2010. Diagrams is an international and interdisciplinary conference series, which continues to present the very best work in all aspects of research on the theory and application of diagrams. Some key questions that researchers are tackling concern gaining an insight into how diagrams are used, how they are rep- sented, which types are available and when it is appropriate to use them. The use of diagrammatic notations is studied for a variety of purposes including communication, cognition, creative thought, computation and problem-solving. Clearly, this must be pursued as an interdisciplinary endeavor, and Diagrams is the only conference series that provides such a united forum for all areas that are concerned with the study of diagrams: for example, architecture, arti?cial intelligence,cartography,cognitivescience,computer science,education,graphic design, history of science, human–computer interaction, linguistics, logic, ma- ematics, philosophy, psychology, and software modelling. The articles in this volume re?ect this variety and interdisciplinarity of the ?eld.

Point Processes and Their Statistical Inference Second Edition

Author : Alan Karr
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Introduction to Statistical Inference

Author : E. S. Keeping
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This excellent text emphasizes the inferential and decision-making aspects of statistics. The first chapter is mainly concerned with the elements of the calculus of probability. Additional chapters cover the general properties of distributions, testing hypotheses, and more.

Differential Geometry in Statistical Inference

Author : Shun'ichi Amari
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Comparative Statistical Inference

Author : Vic Barnett
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This fully updated and revised third edition, presents a wide ranging, balanced account of the fundamental issues across the full spectrum of inference and decision-making. Much has happened in this field since the second edition was published: for example, Bayesian inferential procedures have not only gained acceptance but are often the preferred methodology. This book will be welcomed by both the student and practising statistician wishing to study at a fairly elementary level, the basic conceptual and interpretative distinctions between the different approaches, how they interrelate, what assumptions they are based on, and the practical implications of such distinctions. As in earlier editions, the material is set in a historical context to more powerfully illustrate the ideas and concepts. Includes fully updated and revised material from the successful second edition Recent changes in emphasis, principle and methodology are carefully explained and evaluated Discusses all recent major developments Particular attention is given to the nature and importance of basic concepts (probability, utility, likelihood etc) Includes extensive references and bibliography Written by a well-known and respected author, the essence of this successful book remains unchanged providing the reader with a thorough explanation of the many approaches to inference and decision making.