Improving Statistical Reasoning

Author: Peter Sedlmeier
Publisher: Psychology Press
ISBN: 1135705763
Format: PDF, Kindle
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This book focuses on how statistical reasoning works and on training programs that can exploit people's natural cognitive capabilities to improve their statistical reasoning. Training programs that take into account findings from evolutionary psychology and instructional theory are shown to have substantially larger effects that are more stable over time than previous training regimens. The theoretical implications are traced in a neural network model of human performance on statistical reasoning problems. This book apppeals to judgment and decision making researchers and other cognitive scientists, as well as to teachers of statistics and probabilistic reasoning.

Acute Lung Injury

Author: J.J. Marini
Publisher: Springer Science & Business Media
ISBN: 3642607330
Format: PDF, Mobi
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To integrate current knowledge in terms of basic and clinical science and to highlight problems, thirty world-renowned experts in the field of acute lung injury describe the state of up to date knowledge regarding the epidemiology, pathophysiology, and clinical management of acute lung injury. Novel techniques for the clinical support of these difficult patients are discussed in full. Prospects for successful pharmacological intervention are also outlined. This book is aimed at those practising within the field of critical care and is likely to become an indispensable aid to all concerned with the investigation and management of patients with severe respiratory failure.

Current Trends in Knowledge Acquisition

Author: Bob Wielinga
Publisher: IOS Press
ISBN: 9789051990362
Format: PDF, ePub, Docs
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Knowledge acquisition has become a major area of artificial intelligence and cognitive science research. The papers in this book show that the area of knowledge acquisition for knowledge-based systems is still a diverse field in which a large number of research topics are being addressed. However, several main themes run through the papers. First, the issues of integrating knowledge from different sources and K.A. tools is a salient topic in many papers. A second major topic in the papers is that of knowledge modelling. Research in knowledge-based systems emphasises the use of generic models of reasoning and its underlying knowledge. An important trend in the area of knowledge modelling aims at the formalisation of knowledge models. Where the field of knowledge acquisition was without tools and techniques years ago, now there is a rapidly growing body of techniques and tools. Apart from the integrated workbenches already mentioned above, several papers in this book present new tools. Although knowledge acquisition and machine learning have been considered as separate subfields of AI, there is a tendency for the two fields to come together. This publication combines machine learning techniques with more conventional knowledge elicitation techniques. A framework is presented in which reasoning, problem solving and learning together form a knowledge intensive system that can acquire knowledge from its own experience.

Judgment Under Uncertainty

Author: Daniel Kahneman
Publisher: Cambridge University Press
ISBN: 9780521284141
Format: PDF, Mobi
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The thirty-five chapters in this book describe various judgmental heuristics and the biases they produce, not only in laboratory experiments but in important social, medical, and political situations as well. Individual chapters discuss the representativeness and availability heuristics, problems in judging covariation and control, overconfidence, multistage inference, social perception, medical diagnosis, risk perception, and methods for correcting and improving judgments under uncertainty. About half of the chapters are edited versions of classic articles; the remaining chapters are newly written for this book. Most review multiple studies or entire subareas of research and application rather than describing single experimental studies. This book will be useful to a wide range of students and researchers, as well as to decision makers seeking to gain insight into their judgments and to improve them.

Theory of Decision under Uncertainty

Author: Itzhak Gilboa
Publisher: Cambridge University Press
ISBN: 9780521741231
Format: PDF, Docs
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This book describes the classical axiomatic theories of decision under uncertainty, as well as critiques thereof and alternative theories. It focuses on the meaning of probability, discussing some definitions and surveying their scope of applicability. The behavioral definition of subjective probability serves as a way to present the classical theories, culminating in Savage's theorem. The limitations of this result as a definition of probability lead to two directions - first, similar behavioral definitions of more general theories, such as non-additive probabilities and multiple priors, and second, cognitive derivations based on case-based techniques.