Thinking Things Through provides a broad, historical, and rigorous introduction to the logical tradition in philosophy and to its contemporary significance. The presentation is centered around three of the most fruitful issues in Western thought: What are proofs, and why do they provide knowledge? How can experience be used to gain knowledge or to alter beliefs in a rational way? What is the nature of mind and of mental events and mental states? In a clear and lively style, Glymour describes these key philosophical problems and traces attempts to solve them, from ancient Greece to the present. Thinking Things Through reveals the philosophical sources of modern work in logic, the theory of computation, Bayesian statistics, cognitive psychology, and artificial intelligence, and it connects these subjects with contemporary problems in epistemology and metaphysics. The text is full of examples and problems, and an instructor's manual is available.Clark Glymour is Alumni Professor of Philosophy at Carnegie-Mellon University and Adjunct Professor of History and Philosophy of Science at the University of Pittsburgh.
What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control our environment? In this book Peter Spirtes, Clark Glymour, and Richard Scheines address these questions using the formalism of Bayes networks, with results that have been applied in diverse areas of research in the social, behavioral, and physical sciences. The authors show that although experimental and observational study designs may not always permit the same inferences, they are subject to uniform principles. They axiomatize the connection between causal structure and probabilistic independence, explore several varieties of causal indistinguishability, formulate a theory of manipulation, and develop asymptotically reliable procedures for searching over equivalence classes of causal models, including models of categorical data and structural equation models with and without latent variables. The authors show that the relationship between causality and probability can also help to clarify such diverse topics in statistics as the comparative power of experimentation versus observation, Simpson's paradox, errors in regression models, retrospective versus prospective sampling, and variable selection. The second edition contains a new introduction and an extensive survey of advances and applications that have appeared since the first edition was published in 1993.
This title provides an introduction to assumptions, algorithms, and techniques of causal Bayes nets and graphical causal models in the context of psychological examples. It demonstrates their potential as a powerful tool for guiding experimental inquiry.
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.