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In Defence of Objective Bayesianism
Objective Bayesianism is a methodological theory that is currently applied in statistics, philosophy, artificial intelligence, physics and other sciences. This book develops the formal and philosophical foundations of the theory, at a level accessible to a graduate student with some familiarity with mathematical notation.
Bayesian Nets and Causality: Philosophical and Computational Foundations
Bayesian nets are used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions perform diagnoses, take decisions and even to discover causal relationships. This book brings together how to automate reasoning in artificial intelligence, and the nature of causality and probability in philosophy.
Causality in the Sciences
Why do ideas of how mechanisms relate to causality and probability differ so much across the sciences? Can progress in understanding the tools of causal inference in some sciences lead to progress in others? This book tackles these questions and others concerning the use of causality in the sciences.
Lectures on Inductive Logic
Inductive logic is a theory of how one should reason in the face of uncertainty. It has applications to decision making and artificial intelligence, as well as to scientific problems.
Probabilistic Logics and Probabilistic Networks
While probabilistic logics in principle might be applied to solve a range of problems, in practice they are rarely applied - perhaps because they seem disparate, complicated, and computationally intractable. This programmatic book argues that several approaches to probabilistic logic fit into a simple unifying framework in which logically complex evidence is used to associate probability intervals or probabilities with sentences. Specifically, Part I shows that there is a natural way to present a question posed in probabilistic logic, and that various inferential procedures provide semantics for that question, while Part II shows that there is the potential to develop computationally feasible methods to mesh with this framework. The book is intended for researchers in philosophy, logic, computer science and statistics. A familiarity with mathematical concepts and notation is presumed, but no advanced knowledge of logic or probability theory is required.
Exploratory Causal Analysis with Time Series Data
Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments. Data analysis techniques are required for identifying causal information and relationships directly from such observational data. This need has led to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in time series data sets. Exploratory causal analysis (ECA) provides a framework for exploring potential causal str...
History of Exercise Physiology
Well illustrated with figures and photos, this text brings together leading authorities in exercise physiology to help readers understand the research findings and meet the most prominent professionals in the field.
Evidence Contestation
This book examines the practices of contesting evidence in democratically constituted knowledge societies. It provides a multifaceted view of the processes and conditions of evidence criticism and how they determine the dynamics of de- and re-stabilization of evidence. Evidence is an essential resource for establishing claims of validity, resolving conflicts, and legitimizing decisions. In recent times, however, evidence is being contested with increasing frequency. Such contestations vary in form and severity – from questioning the interpretation of data or the methodological soundness of studies to accusations of evidence fabrication. The contributors to this volume explore which actors,...
Philosophical Theories of Probability
The Twentieth Century has seen a dramatic rise in the use of probability and statistics in almost all fields of research. This has stimulated many new philosophical ideas on probability. Philosophical Theories of Probability is the first book to present a clear, comprehensive and systematic account of these various theories and to explain how they relate to one another. Gillies also offers a distinctive version of the propensity theory of probability, and the intersubjective interpretation, which develops the subjective theory.
Induction and Deduction in the Sciences
The articles in this volume deal with the main inferential methods that can be applied to different kinds of experimental evidence. These contributions - accompanied with critical comments - by renowned scholars in the field of philosophy of science aim at removing the traditional opposition between inductivists and deductivists. They explore the different methods of explanation and justification in the sciences in different contexts and with different objectives. The volume contains contributions on methods of the sciences, especially on induction, deduction, abduction, laws, probability and explanation, ranging from logic, mathematics, natural to the social sciences. They present a highly topical pluralist re-evaluation of methodological and foundational procedures and reasoning, e.g. focusing in Bayesianism and Artificial Intelligence. They document the second international conference in Vienna on "Induction and Deduction in the Sciences" as part of the Scientific Network on "Historical and Contemporary Perspectives of Philosophy of Science in Europe", funded by the European Science Foundation (ESF).
