Download or read book Information Inference and Decision written by G. Menges and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Under the title 'Information, Inference and Decision' this volume in the Theory and Decision Library presents some papers on issues from the borderland of statistical inference philosophy and epistemology, written by statisticians and decision theorists who belonged or are allied to the former Saarbriicken school of statistical decision theory. In the first part I make an attempt to outline an objective theory of inductive behaviour, on the basis of R. A. Fisher's statistical inference philosophy, on the one hand, and R. Carnap's inductive logic, on the other. A special problem arising in the context of the new theory, viz., the problem of vagueness of concepts (in particular in the social sciences) is treated separately by H. Skala and myself. B. Leiner has contributed some biographical and bibliographical notes on the objective theory of inductive behaviour. Part II is concerned with inference philosophy. D. A. S. Fraser, the founder of structural inference theory, characterizes and compares some inference philosophies, and discusses his own and the arguments of the critics of his structural theory. In my opinion, Fraser's structural infer ence theory is suited to complete Fisher's inference philosophy in some essential points, if not to replace it. An interesting task for future re search work is to establish the connection between Fraser's theory and Carnap's ideas in the framework of an objective theory of inductive behaviour.
Download or read book An Introduction to Bayesian Inference and Decision written by Robert L. Winkler and published by Probabilistic Pub. This book was released on 2003-01-01 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: CD-ROM contains: Beta Distribution Generator (Excel file) ; Binomial Distribution Generator (Excel file) ; book exercises (MS Word files) ; book figures (Powerpoint files) ; TreeAge Data decision trees for some of the examples in the book ; Demonstration versions of TreeAge Data and Lumina Analytica.
Download or read book Theory of Statistical Inference and Information written by Igor Vajda and published by Springer. This book was released on 1989-02-28 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Information Theory Inference and Learning Algorithms written by David J. C. MacKay and published by Cambridge University Press. This book was released on 2003-09-25 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book On Science Inference Information and Decision Making written by A. Szaniawski and published by Springer Science & Business Media. This book was released on 1998-09-30 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are two competing pictures of science. One considers science as a system of inferences, whereas another looks at science as a system of actions. The essays included in this collection offer a view which intends to combine both pictures. This compromise is well illustrated by Szaniawski's analysis of statistical inferences. It is shown that traditional approaches to the foundations of statistics do not need to be regarded as conflicting with each other. Thus, statistical rules can be treated as rules of behaviour as well as rules of inference. Szaniawski's uniform approach relies on the concept of rationality, analyzed from the point of view of decision theory. Applications of formal tools to the problem of justice and division of goods shows that the concept of rationality has a wider significance. Audience: The book will be of interest to philosophers of science, logicians, ethicists and mathematicians.
Download or read book On Science Inference Information and Decision Making written by A. Szaniawski and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are two competing pictures of science. One considers science as a system of inferences, whereas another looks at science as a system of actions. The essays included in this collection offer a view which intends to combine both pictures. This compromise is well illustrated by Szaniawski's analysis of statistical inferences. It is shown that traditional approaches to the foundations of statistics do not need to be regarded as conflicting with each other. Thus, statistical rules can be treated as rules of behaviour as well as rules of inference. Szaniawski's uniform approach relies on the concept of rationality, analyzed from the point of view of decision theory. Applications of formal tools to the problem of justice and division of goods shows that the concept of rationality has a wider significance. Audience: The book will be of interest to philosophers of science, logicians, ethicists and mathematicians.
Download or read book Bayesian Inference written by Hanns Ludwig Harney and published by Springer. This book was released on 2016-10-18 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. This is particularly useful when the observed parameter is barely above the background or the histogram of multiparametric data contains many empty bins, so that the determination of the validity of a theory cannot be based on the chi-squared-criterion. In addition to the solutions of practical problems, this approach provides an epistemic insight: the logic of quantum mechanics is obtained as the logic of unbiased inference from counting data. New sections feature factorizing parameters, commuting parameters, observables in quantum mechanics, the art of fitting with coherent and with incoherent alternatives and fitting with multinomial distribution. Additional problems and examples help deepen the knowledge. Requiring no knowledge of quantum mechanics, the book is written on introductory level, with many examples and exercises, for advanced undergraduate and graduate students in the physical sciences, planning to, or working in, fields such as medical physics, nuclear physics, quantum mechanics, and chaos.
Download or read book Inference and Decision written by Günter Menges and published by University Press of Canada ; Delhi : Hindustan Publishing Corporation. This book was released on 1973 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Inference Method and Decision written by R.D. Rosenkrantz and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Foundations of Info metrics written by Amos Golan and published by Oxford University Press. This book was released on 2018 with total page 489 pages. Available in PDF, EPUB and Kindle. Book excerpt: Info-metrics is the science of modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is at the intersection of information theory, statistical inference, and decision-making under uncertainty. It plays an important role in helping make informed decisions even when there is inadequate or incomplete information because it provides a framework to process available information with minimal reliance on assumptions that cannot be validated. In this pioneering book, Amos Golan, a leader in info-metrics, focuses on unifying information processing, modeling and inference within a single constrained optimization framework. Foundations of Info-Metrics provides an overview of modeling and inference, rather than a problem specific model, and progresses from the simple premise that information is often insufficient to provide a unique answer for decisions we wish to make. Each decision, or solution, is derived from the available input information along with a choice of inferential procedure. The book contains numerous multidisciplinary applications and case studies, which demonstrate the simplicity and generality of the framework in real world settings. Examples include initial diagnosis at an emergency room, optimal dose decisions, election forecasting, network and information aggregation, weather pattern analyses, portfolio allocation, strategy inference for interacting entities, incorporation of prior information, option pricing, and modeling an interacting social system. Graphical representations illustrate how results can be visualized while exercises and problem sets facilitate extensions. This book is this designed to be accessible for researchers, graduate students, and practitioners across the disciplines.
Download or read book Inference and Decision making with Heterogeneous Information written by Jingqi Yu and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Every day, people are bombarded with information from various sources, and yet they do not have nearly enough time to process it. How do people sift through information and decide what to use, and what do they rely on to make these decisions? How do people respond to inconsistent or conflicting information? The goal of this dissertation is to investigate these core questions as well as their implications in education and business. To do this, my work takes a highly interdisciplinary approach that combines cognitive science, consumer behavior, information systems, and communication studies, using a blend of behavioral experimentation and computational cognitive modeling. I present three papers that examine the mechanisms people engage in when they integrate information displayed in different forms and from different sources in educational and consumer contexts. The first paper approaches learning statistical inference in an experientially grounded way by developing computer simulations. It reveals people's flexibility to "game" the game, highlighting the importance of ensuring alignment between visual training and learning objectives in educational games. The second paper uses a computational approach to systematically reveal the common ways people ascribe meanings to the five-star rating system when shopping online. The findings suggest two ways to improve the interactions between reputation and feedback systems and their users: normalizing ratings with commentaries and normalizing ratings with clarification and education. The third paper demonstrates how people integrate ratings and reviews into their purchase decisions, and how these decisions can be influenced by the consumers' justifications. It also unveils the role of information relevance and similarity in social cognition. These insights could be leveraged by different players in the market to influence consumer choice. By examining information integration in education and digital economy, this dissertation helps create a more comprehensive picture of how people generate, disseminate, and consume information. It highlights the mechanisms by which people integrate heterogeneous information to make inferences and decisions, as well as cues and heuristics they rely on to facilitate these everyday tasks. This expanded understanding informs the development of systems whose goal is to facilitate user navigation in the era of big data.
Download or read book Statistical and Inductive Inference by Minimum Message Length written by C.S. Wallace and published by Springer Science & Business Media. This book was released on 2005-05-26 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Minimum Message Length (MML) Principle is an information-theoretic approach to induction, hypothesis testing, model selection, and statistical inference. MML, which provides a formal specification for the implementation of Occam's Razor, asserts that the ‘best’ explanation of observed data is the shortest. Further, an explanation is acceptable (i.e. the induction is justified) only if the explanation is shorter than the original data. This book gives a sound introduction to the Minimum Message Length Principle and its applications, provides the theoretical arguments for the adoption of the principle, and shows the development of certain approximations that assist its practical application. MML appears also to provide both a normative and a descriptive basis for inductive reasoning generally, and scientific induction in particular. The book describes this basis and aims to show its relevance to the Philosophy of Science. Statistical and Inductive Inference by Minimum Message Length will be of special interest to graduate students and researchers in Machine Learning and Data Mining, scientists and analysts in various disciplines wishing to make use of computer techniques for hypothesis discovery, statisticians and econometricians interested in the underlying theory of their discipline, and persons interested in the Philosophy of Science. The book could also be used in a graduate-level course in Machine Learning and Estimation and Model-selection, Econometrics and Data Mining. C.S. Wallace was appointed Foundation Chair of Computer Science at Monash University in 1968, at the age of 35, where he worked until his death in 2004. He received an ACM Fellowship in 1995, and was appointed Professor Emeritus in 1996. Professor Wallace made numerous significant contributions to diverse areas of Computer Science, such as Computer Architecture, Simulation and Machine Learning. His final research focused primarily on the Minimum Message Length Principle.
Download or read book Statistical Inference as Severe Testing written by Deborah G. Mayo and published by Cambridge University Press. This book was released on 2018-09-20 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Statistical Decision Rules and Optimal Inference written by N. N. Cencov and published by American Mathematical Soc.. This book was released on 2000-04-19 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: None available in plain English.
Download or read book Algorithms for Decision Making written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2022-08-16 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them. Automated decision-making systems or decision-support systems—used in applications that range from aircraft collision avoidance to breast cancer screening—must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through interaction with the environment; state uncertainty, in which we do not know the current state of the environment due to imperfect perceptual information; and decision contexts involving multiple agents. The book focuses primarily on planning and reinforcement learning, although some of the techniques presented draw on elements of supervised learning and optimization. Algorithms are implemented in the Julia programming language. Figures, examples, and exercises convey the intuition behind the various approaches presented.
Download or read book The Cambridge Handbook of the Law of Algorithms written by Woodrow Barfield and published by Cambridge University Press. This book was released on 2020-11-05 with total page 1327 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms are a fundamental building block of artificial intelligence - and, increasingly, society - but our legal institutions have largely failed to recognize or respond to this reality. The Cambridge Handbook of the Law of Algorithms, which features contributions from US, EU, and Asian legal scholars, discusses the specific challenges algorithms pose not only to current law, but also - as algorithms replace people as decision makers - to the foundations of society itself. The work includes wide coverage of the law as it relates to algorithms, with chapters analyzing how human biases have crept into algorithmic decision-making about who receives housing or credit, the length of sentences for defendants convicted of crimes, and many other decisions that impact constitutionally protected groups. Other issues covered in the work include the impact of algorithms on the law of free speech, intellectual property, and commercial and human rights law.
Download or read book The Effects of Information Load on Inferences in Decision Making in a Decision Support System Environment written by Russell K. H. Ching and published by . This book was released on 1994 with total page 604 pages. Available in PDF, EPUB and Kindle. Book excerpt:
