These provocative essays by leading philosophers of science exemplify and illuminate the contemporary uncertainty and excitement in the field. The papers are rich in new perspectives, and their far-reaching criticisms challenge arguments long prevalent in classic philosophical problems of induction, empiricism, and realism. By turns empirical or analytic, historical or programmatic, confessional or argumentative, the authors' arguments both describe and demonstrate the fact that philosophy of science is in a ferment more intense than at any time since the heyday of logical positivism early in the twentieth century. Contents: “Thoroughly Modern Meno,” Clark Glymour and Kevin Kelly “The Concept of Induction in the Light of the Interrogative Approach to Inquiry,” Jaakko Hintikka “Aristotelian Natures and Modern Experimental Method,” Nancy Cartwright “Genetic Inference: A Reconsideration of “David Hume's Empiricism,” Barbara D. Massey and Gerald J. Massey “Philosophy and the Exact Sciences: Logical Positivism as a Case Study,” Michael Friedman “Language and Interpretation: Philosophical Reflections and Empirical Inquiry,” Noam Chomsky “Constructivism, Realism, and Philosophical Method,” Richard Boyd “Do We Need a Hierarchical Model of Science?” Diderik Batens “Theories of Theories: A View from Cognitive Science,” Richard E. Grandy “Procedural Syntax for Theory Elements,” Joseph D. Sneed “Why Functionalism Didn't Work,” Hilary Putnam “Physicalism,” Hartry Field This title is part of UC Press's Voices Revived program, which commemorates University of California Press’s mission to seek out and cultivate the brightest minds and give them voice, reach, and impact. Drawing on a backlist dating to 1893, Voices Revived makes high-quality, peer-reviewed scholarship accessible once again using print-on-demand technology. This title was originally published in 1992.

INFERENCE PROCEDURES AND DESIGNS ARE DISCUSSED THAT ARE MORE APPROPRIATE TO THOSE SITUATIONS IN ANOVA or MANOVA where, against the customary or standard null hypothesis, we are interested in certain on-standard alternatives or, in other words, we are interested in increasing the discrimination along certain (non-standard) directions of deviation, even at some cost to the discrimination along other directions of deviation. It is indicated how, for several such situations, it is possible to impr ve upon both the customary designs and the customary inference procedure. (Author).

Bayesian inference facilitated structured interpretation of a nonreplicated, experience-based survey of potential nesting habitat for bald eagles (Haliaeetus leucocephalus) along the five Great Lakes shorelines. We developed a pattern recognition (PATREC) model of our aerial search image with six habitat attributes: (a) tree cover, (b) proximity and (c) type/amount of human disturbance, (d) potential foraging habitat/shoreline irregularity, and suitable trees for (e) perching and (f) nesting. Tree cover greater than 10 percent, human disturbance more than 0.8 km away, a ratio of total to linear shoreline distance greater than 2.0, and suitable perch and nest trees were prerequisite for good eagle habitat (having sufficient physical attributes for bald eagle nesting). The estimated probability of good habitat was high (96 percent) when all attributes were optimal, and nonexistent (0 percent) when none of the model attributes were present. Of the 117 active bald eagle nests along the Great Lakes shorelines in 1992, 82 percent were in habitat classified as good. While our PATREC model provides a method for consistent interpretation of subjective surveyor experience, it also facilitates future management of bald eagle nesting habitat along Great Lakes shorelines by providing insight into the number, type, and relative importance of key habitat attributes. This practical application of Bayesian inference demonstrates the technique's advantages for effectively incorporating available expertise, detailing model development processes, enabling exploratory simulations, and facilitating long-term ecosystem monitoring.

In the last decade, there have been an increasing convergence of interest and methods between theoretical physics and fields as diverse as probability, machine learning, optimization and compressed sensing. In particular, many theoretical and applied works in statistical physics and computer science have relied on the use of message passing algorithms and their connection to statistical physics of spin glasses. The aim of this book, especially adapted to PhD students, post-docs, and young researchers, is to present the background necessary for entering this fast developing field.

Inference control in statistical databases, also known as statistical disclosure limitation or statistical confidentiality, is about finding tradeoffs to the tension between the increasing societal need for accurate statistical data and the legal and ethical obligation to protect privacy of individuals and enterprises which are the source of data for producing statistics. Techniques used by intruders to make inferences compromising privacy increasingly draw on data mining, record linkage, knowledge discovery, and data analysis and thus statistical inference control becomes an integral part of computer science. This coherent state-of-the-art survey presents some of the most recent work in the field. The papers presented together with an introduction are organized in topical sections on tabular data protection, microdata protection, and software and user case studies.

The design inference uncovers intelligent causes by isolating their key trademark: specified events of small probability. Just about anything that happens is highly improbable, but when a highly improbable event is also specified (i.e. conforms to an independently given pattern) undirected natural causes lose their explanatory power. Design inferences can be found in a range of scientific pursuits from forensic science to research into the origins of life to the search for extraterrestrial intelligence. This challenging and provocative 1998 book shows how incomplete undirected causes are for science and breathes new life into classical design arguments. It will be read with particular interest by philosophers of science and religion, other philosophers concerned with epistemology and logic, probability and complexity theorists, and statisticians.

Educational policy-makers around the world constantly make decisions about how to use scarce resources to improve the education of children. Unfortunately, their decisions are rarely informed by evidence on the consequences of these initiatives in other settings. Nor are decisions typically accompanied by well-formulated plans to evaluate their causal impacts. As a result, knowledge about what works in different situations has been very slow to accumulate.Over the last several decades, advances in research methodology, administrative record keeping, and statistical software have dramatically increased the potential for researchers to conduct compelling evaluations of the causal impacts of educational interventions, and the number of well-designed studies is growing. Written in clear, concise prose, Methods Matter: Improving Causal Inference in Educational and Social Science Research offers essential guidance for those who evaluate educational policies. Using numerous examples of high-quality studies that have evaluated the causal impacts of important educational interventions, the authors go beyond the simple presentation of new analytical methods to discuss the controversies surrounding each study, and provide heuristic explanations that are also broadly accessible. Murnane and Willett offer strong methodological insights on causal inference, while also examining the consequences of a wide variety of educational policies implemented in the U.S. and abroad. Representing a unique contribution to the literature surrounding educational research, this landmark text will be invaluable for students and researchers in education and public policy, as well as those interested in social science.