Philosophy of Statistics

Statisticians and philosophers of science have many common interests but restricted communication with each other. This volume aims to remedy these shortcomings. It provides state-of-the-art research in the area of philosophy of statistics by encouraging numerous experts to communicate with one another without feeling "restricted by their disciplines or thinking "piecemeal in their treatment of issues. A second goal of this book is to present work in the field without bias toward any particular statistical paradigm. Broadly speaking, the essays in this Handbook are concerned with problems of induction, statistics and probability. For centuries, foundational problems like induction have been among philosophers' favorite topics; recently, however, non-philosophers have increasingly taken a keen interest in these issues. This volume accordingly contains papers by both philosophers and non-philosophers, including scholars from nine academic disciplines.Provides a bridge between philosophy and current scientific findingsCovers theory and applicationsEncourages multi-disciplinary dialogue

Inhalt
Elementary Probability and Statistics: A Primer Conditional Probability, by Alan Hájek The Varieties of Conditional Probability Paradigm Error Statistics Significance Testing The Bayesian Decision-Theoretic Approach to Statistics Modern Bayesian Inference: Foundations and Objective Methods Evidential Probability and Objective Bayesian Epistemology Confirmation Theory Challenges to Bayesian Confirmation Theory Bayesianism as a Pure Logic of Inference Bayesian Inductive Logic, Verisimilitude, and Statistics Likelihood and its Evidential Framework Evidence, Evidence Functions, and Error Probabilities AIC Scores as Evidence - a Bayesian Interpretation The Likelihood Principle AIC, BIC and Recent Advances in Model Selection Posterior Model Probabilities Defining Randomness Mathematical Foundations of Randomness Paradoxes of Probability Statistical Paradoxes: Take It to The Limit Statistics as Inductive Inference Common Cause in Causal Inference The Logic and Philosophy of Causal Inference: A Statistical Perspective Statistical Learning Theory as a Framework for the Philosophy of Induction Testability and Statistical Learning Theory Luckiness and Regret in Minimum Description Length Inference MML, Hybrid Bayesian Network Graphical Models, Statistical, by Consistency, Invariance and Uniqueness Simplicity, Truth and Probability Normal Approximations Stein's Phenomenon Data, Data, Everywhere: Statistical Issues in Data Mining An Application of Statistics in Climate Change: Detection of Nonlinear Changes in a Streamflow Timing Measure in the Columbia and Missouri Headwaters The Subjective and the Objective Probability in Ancient India

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