This Element explores the Bayesian approach to the logic and epistemology of scientific reasoning. Section 1 introduces the probability calculus as an appealing generalization of classical logic for uncertain reasoning. Section 2 explores some of the vast terrain of Bayesian epistemology. Three epistemological postulates suggested by Thomas Bayes in his seminal work guide the e...(展开全部)

UNDERSTANDING SCIENTIFIC REASONING develops critical reasoning skills and guides students in the improvement of their scientific and technological literacy. The authors teach students how to understand and critically evaluate the scientific information they encounter in both textbooks and the popular media. With its focus on scientific pedagogy, UNDERSTANDING SCIENTIFIC REASONI...(展开全部)

In this clearly reasoned defense of Bayes's Theorem — that probability can be used to reasonably justify scientific theories — Colin Howson and Peter Urbach examine the way in which scientists appeal to probability arguments, and demonstrate that the classical approach to statistical inference is full of flaws. Arguing the case for the Bayesian method with little more than basi...(展开全部)

In this clearly reasoned defense of Bayes's Theorem — that probability can be used to reasonably justify scientific theories — Colin Howson and Peter Urbach examine the way in which scientists appeal to probability arguments, and demonstrate that the classical approach to statistical inference is full of flaws. Arguing the case for the Bayesian method with little more than basi...(展开全部)

"A concise and useful guide, which provides an overview of the skills necessary to good reviewing ...Dealing clearly with traditional sources such as books and journals alongside recent developments such as wikis, blogs, and other online material, this handbook provides students with not only the basics, but guidance on the evolving issues in research dissemination and literatu...(展开全部)

Judea Pearl is professor of computer science and statistics at the University of California, Los Angeles, where he directs the Cognitive Systems Laboratory and conducts research in artificial intelligence, human reasoning, and philosophy of science. The author of Heuristics and Probabilistic Reasoning, he is a member of the National Academy of Engineering and a Founding Fellow ...(展开全部)

David Barber is Reader in Information Processing in the Department of Computer Science, University College London.

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens th...(展开全部)

Written by one of the preeminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, economics, philosophy, cognitive science, and the health and social scienc...(展开全部)

What makes an argument in a law case good or bad? Can legal decisions be justified by purely rational argument or are they ultimately determined by more subjective influences? These questions are central to the study of jurisprudence, and are thoroughly and critically examined in Legal Reasoning and Legal Theory, now with a new and up-to-date foreword. Its clarity of explanatio...(展开全部)