Tychomancy—meaning “the divination of chances”—presents a set of rules for inferring the physical probabilities of outcomes from the causal or dynamic properties of the systems that produce them. Probabilities revealed by the rules are wide-ranging: they include the probability of getting a 5 on a die roll, the probability distributions found in statistical physics, and the probabilities that underlie many prima facie judgments about fitness in evolutionary biology. Michael Strevens makes three claims about the rules. First, they are reliable. Second, they are known, though not fully consciously, to all human beings: they constitute a key part of the physical intuition that allows us to navigate around the world safely in the absence of formal scientific knowledge. Third, they have played a crucial but unrecognized role in several major scientific innovations. A large part of Tychomancy is devoted to this historical role for probability inference rules. Strevens first analyzes James Clerk Maxwell’s extraordinary, apparently a priori, deduction of the molecular velocity distribution in gases, which launched statistical physics. Maxwell did not derive his distribution from logic alone, Strevens proposes, but rather from probabilistic knowledge common to all human beings, even infants as young as six months old. Strevens then turns to Darwin’s theory of natural selection, the statistics of measurement, and the creation of models of complex systems, contending in each case that these elements of science could not have emerged when or how they did without the ability to “eyeball” the values of physical probabilities.

The story of Kebister was a constant surprise to archaeologists and has opened a remarkable window on 4000 years of Shetland's past.

Anyone involved in the philosophy of science is naturally drawn into the study of the foundations of probability. Different interpretations of probability, based on competing philosophical ideas, lead to different statistical techniques, and frequently to mutually contradictory consequences.This unique book presents a new interpretation of probability, rooted in the traditional interpretation that was current in the 17th and 18th centuries. Mathematical models are constructed based on this interpretation, and statistical inference and decision theory are applied, including some examples in artificial intelligence, solving the main foundational problems. Nonstandard analysis is extensively developed for the construction of the models and in some of the proofs. Many nonstandard theorems are proved, some of them new, in particular, a representation theorem that asserts that any stochastic process can be approximated by a process defined over a space with equiprobable outcomes.

This classroom-tested textbook is an introduction to probability theory, with the right balance between mathematical precision, probabilistic intuition, and concrete applications. Introduction to Probability covers the material precisely, while avoiding excessive technical details. After introducing the basic vocabulary of randomness, including events, probabilities, and random variables, the text offers the reader a first glimpse of the major theorems of the subject: the law of large numbers and the central limit theorem. The important probability distributions are introduced organically as they arise from applications. The discrete and continuous sides of probability are treated together to emphasize their similarities. Intended for students with a calculus background, the text teaches not only the nuts and bolts of probability theory and how to solve specific problems, but also why the methods of solution work.

An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

A Stanford University Press classic.

Constitutes the proceedings of the Seventh Latin American Symposium on Mathematical Logic, held July 29-August 2, 1985, at the University of Campinas in Brazil. This book offers an introduction to the active lines of research in mathematical logic and emphasizes the connections to other fields - philosophy, computer science and probability theory.

In Reading Contingency: The Accident in Contemporary Fiction, David Wylot constructs an innovative study of the relationship between plotted accidents in twenty-first century British and American fiction, the phenomenology of reading, and a contemporary experience of time that is increasingly understood to be contingent and accidental. A synthesis of literary and cultural analysis, narratology, critical theories of time and the philosophy of contingency, the book explores the accident’s imagination of contemporary time and the relationship between reading and living in novels by writers including A.M. Homes, Nicola Barker, Noah Hawley, J.M. Coetzee, J.G. Ballard, Jesmyn Ward, Jennifer Egan, and Tom McCarthy.