The R E A L Model Rethinking Generalization

A Practitioner's Guide to Teach for Generalization Within ABA Treatment for Autism and Other Disabilities

This volume examines the limitations of mathematical logic and proposes a new approach to logic intended to overcome them. To this end, the book compares mathematical logic with earlier views of logic, both in the ancient and in the modern age, including those of Plato, Aristotle, Bacon, Descartes, Leibniz, and Kant. From the comparison it is apparent that a basic limitation of mathematical logic is that it narrows down the scope of logic confining it to the study of deduction, without providing tools for discovering anything new. As a result, mathematical logic has had little impact on scientific practice. Therefore, this volume proposes a view of logic according to which logic is intended, first of all, to provide rules of discovery, that is, non-deductive rules for finding hypotheses to solve problems. This is essential if logic is to play any relevant role in mathematics, science and even philosophy. To comply with this view of logic, this volume formulates several rules of discovery, such as induction, analogy, generalization, specialization, metaphor, metonymy, definition, and diagrams. A logic based on such rules is basically a logic of discovery, and involves a new view of the relation of logic to evolution, language, reason, method and knowledge, particularly mathematical knowledge. It also involves a new view of the relation of philosophy to knowledge. This book puts forward such new views, trying to open again many doors that the founding fathers of mathematical logic had closed historically. trigger

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

Integrating humanism and behaviorism, this volume presents evidence-based techniques for improving health, safety, and well-being in all walks of life.

This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures.

In choosing between moral alternatives -- choosing between various forms of ethical action -- we typically make calculations of the following kind: A is better than B; B is better than C; therefore A is better than C. These inferences use the principle of transitivity and are fundamental to many forms of practical and theoretical theorizing, not just in moral and ethical theory but in economics. Indeed they are so common as to be almost invisible. What Larry Temkin's book shows is that, shockingly, if we want to continue making plausible judgments, we cannot continue to make these assumptions. Temkin shows that we are committed to various moral ideals that are, surprisingly, fundamentally incompatible with the idea that "better than" can be transitive. His book develops many examples where value judgments that we accept and find attractive, are incompatible with transitivity. While this might seem to leave two options -- reject transitivity, or reject some of our normative commitments in order to keep it -- Temkin is neutral on which path to follow, only making the case that a choice is necessary, and that the cost either way will be high. Temkin's book is a very original and deeply unsettling work of skeptical philosophy that mounts an important new challenge to contemporary ethics.

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Introduces exciting new methods for assessing algorithms for problems ranging from clustering to linear programming to neural networks.