A Chance for Possibility defends the view that the objective modal realm is tripartite: truths about possible worlds supervene on modal truths, which in turn supervene on truths about objective chances. An understanding of supervenience in terms of grounding is developed which — unlike the standard modal characterization — allows the question of what modal truths supervene on to have a non-trivial answer. Relying on this understanding, a negative result is established: modal truths do not supervene on truths about possible worlds, whether possible worlds are conceived of as Lewisian concreta or as abstract objects of some kind. Instead, a conception of pleonastic possible worlds is developed that reverses the direction of supervenience. On the basis of linguistic considerations concerning our use of natural language ‘might’ and ‘might have’ sentences, Steinberg finally argues that truths about objective chances are able to provide a supervenience base for modal truths. A Chance for Possibility is an investigation in analytic metaphysics, drawing on related work in the philosophy of logic and language as well as linguistics. It provides a detailed case study for the fruitful use of a notion of grounding in the clarification and evaluation of longstanding philosophical issues.

It is a commonplace that scientific inquiry makes extensive use of probabilities, many of which seem to be objective chances, describing features of reality that are independent of our minds. Such chances appear to have a number of paradoxical or puzzling features: they appear to be mind-independent facts, but they are intimately connected with rational psychology; they display a temporal asymmetry, but they are supposed to be grounded in physical laws that are time-symmetric; and chances are used to explain and predict frequencies of events, although they cannot be reduced to those frequencies. This book offers an accessible and non-technical introduction to these and other puzzles. Toby Handfield engages with traditional metaphysics and philosophy of science, drawing upon recent work in the foundations of quantum mechanics and thermodynamics to provide a novel account of objective probability that is empirically informed without requiring specialist scientific knowledge.

"Uses simple text and photographs to explain chance and probability"--Provided by publisher.

Probability and Bayesian Modeling is an introduction to probability and Bayesian thinking for undergraduate students with a calculus background. The first part of the book provides a broad view of probability including foundations, conditional probability, discrete and continuous distributions, and joint distributions. Statistical inference is presented completely from a Bayesian perspective. The text introduces inference and prediction for a single proportion and a single mean from Normal sampling. After fundamentals of Markov Chain Monte Carlo algorithms are introduced, Bayesian inference is described for hierarchical and regression models including logistic regression. The book presents several case studies motivated by some historical Bayesian studies and the authors’ research. This text reflects modern Bayesian statistical practice. Simulation is introduced in all the probability chapters and extensively used in the Bayesian material to simulate from the posterior and predictive distributions. One chapter describes the basic tenets of Metropolis and Gibbs sampling algorithms; however several chapters introduce the fundamentals of Bayesian inference for conjugate priors to deepen understanding. Strategies for constructing prior distributions are described in situations when one has substantial prior information and for cases where one has weak prior knowledge. One chapter introduces hierarchical Bayesian modeling as a practical way of combining data from different groups. There is an extensive discussion of Bayesian regression models including the construction of informative priors, inference about functions of the parameters of interest, prediction, and model selection. The text uses JAGS (Just Another Gibbs Sampler) as a general-purpose computational method for simulating from posterior distributions for a variety of Bayesian models. An R package ProbBayes is available containing all of the book datasets and special functions for illustrating concepts from the book. A complete solutions manual is available for instructors who adopt the book in the Additional Resources section.

From New York Times and USA Today Bestselling Author Elisabeth Naughton, the fourth book in her sexy, heart pounding Stolen Series. Even second chances can be stolen… Archaeologist Maren Hudson lost everything when a member of her team was killed during a routine dive. Nine years later she’s rebuilt her life, but a secret from her past forces her back into the career she left behind. Salvage expert Thad Leighton has been in an emotional void since his brother’s murder, but when the opportunity for revenge arises, he can’t say no. Especially since it means being reunited with the one woman he’s never been able to forget. Passion sparks between Maren and Thad as they work the dig together in Mexico. But when Thad uncovers Maren’s secret, the second chance they both thought they’d found is suddenly in jeopardy. Because Thad will have to choose between the vengeance that’s driven him all these years, or a love that has the power to heal them both. "Loved. Loved. Loved. Best in the series!”—Love Affair with an EReader Books in the Stolen Series: STOLEN FURY - Book 1 STOLEN HEAT - Book 2 STOLEN SEDUCTION - Book 3 ​STOLEN CHANCES - Book 4