Originally published in 1952. This book is a critical survey of the views of scientific inference that have been developed since the end of World War I. It contains some detailed exposition of ideas – notably of Keynes – that were cryptically put forward, often quoted, but nowhere explained. Part I discusses and illustrates the method of hypothesis. Part II concerns induction. Part III considers aspects of the theory of probability that seem to bear on the problem of induction and Part IV outlines the shape of this problem and its solution take if transformed by the present approach.

This textbook provides a comprehensive introduction to statistical principles, concepts and methods that are essential in modern statistics and data science. The topics covered include likelihood-based inference, Bayesian statistics, regression, statistical tests and the quantification of uncertainty. Moreover, the book addresses statistical ideas that are useful in modern data analytics, including bootstrapping, modeling of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material for a two-semester course and is intended for master’s students in data science, statistics and computer science with a rudimentary grasp of probability theory. It will also be useful for data science practitioners who want to strengthen their statistics skills.

An inference may be defined as a passage of thought according to some method. In the theory of knowledge it is customary to distinguish deductive and non-deductive inferences. Deductive inferences are truth preserving, that is, the truth of the premises is preserved in the con clusion. As a result, the conclusion of a deductive inference is already 'contained' in the premises, although we may not know this fact until the inference is performed. Standard examples of deductive inferences are taken from logic and mathematics. Non-deductive inferences need not preserve truth, that is, 'thought may pass' from true premises to false conclusions. Such inferences can be expansive, or, ampliative in the sense that the performances of such inferences actually increases our putative knowledge. Standard non-deductive inferences do not really exist, but one may think of elementary inductive inferences in which conclusions regarding the future are drawn from knowledge of the past. Since the body of scientific knowledge is increasing, it is obvious that the method of science must allow non-deductive as well as deductive inferences. Indeed, the explosive growth of science in recent times points to a prominent role for the former. Philosophers of science have long tried to isolate and study the non-deductive inferences in science. The inevitability of such inferences one the one hand, juxtaposed with the poverty of all efforts to identify them, constitutes one of the major cognitive embarrassments of our time.

When his uncle, Michael, dies of AIDS, Joel's dreams and thoughts of Michael keep his memory alive.

Proceedings of an International Research Colloquium held at the University of Western Ontario, 10-13 May 1973.

After its publication in 1967, The Foundations of Scientific Inference taught a generation of students and researchers about the problem of induction, the interpretation of probability, and confirmation theory. Fifty years later, Wesley C. Salmon’s book remains one of the clearest introductions to these fundamental problems in the philosophy of science. With The Foundations of Scientific Inference, Salmon presented a coherent vision of the nature of scientific reasoning, explored the philosophical underpinnings of scientific investigation, and introduced readers to key movements in epistemology and to leading philosophers of the twentieth century—such as Karl Popper, Rudolf Carnap, and Hans Reichenbach—offering a critical assessment and developing his own distinctive views on topics that are still of central importance today. This anniversary edition of Salmon’s foundational work in the philosophy of science features a detailed introduction by Christopher Hitchcock, which examines the book’s origins, influences, and major themes, its impact and enduring effects, the disputes it raised, and its place in current studies, revisiting Salmon’s ideas for a new audience of philosophers, historians, scientists, and students.

Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.