A study of the theory and practice of public relations on the Internet. It offers comprehensive advice on every aspect from understanding the difference between real world and virtual publics to PR tricks and techniques that attract new customers and keep the old ones coming back. It features well-known cases and quotes from the founders of successful Web sites.

Explanatory reasoning is ubiquitous. Not only are rigorous inferences to the best explanation used pervasively in the sciences, this kind of reasoning is common in everyday life. Despite its widespread use, inference to the best explanation is still in need of precise formulation, and it remains controversial. On the one hand, supporters of explanationism take inference to the best explanation to be a justifying form of inference; some even take all justification to be a matter of explanatory reasoning. On the other hand, critics object that inference to the best explanation is not a fundamental form of inference, and some argue that we should be skeptical of inference to the best explanation in general. This volume brings together twenty philosophers to explore various aspects of inference to the best explanation and the debates surrounding it. These specially commissioned essays constitute the cutting edge of research on the role explanatory considerations play in epistemology and philosophy of science.

This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

Machine Learning: A Constraint-Based Approach provides readers with a refreshing look at the basic models and algorithms of machine learning, with an emphasis on current topics of interest that includes neural networks and kernel machines. The book presents the information in a truly unified manner that is based on the notion of learning from environmental constraints. While regarding symbolic knowledge bases as a collection of constraints, the book draws a path towards a deep integration with machine learning that relies on the idea of adopting multivalued logic formalisms, like in fuzzy systems. A special attention is reserved to deep learning, which nicely fits the constrained- based approach followed in this book. This book presents a simpler unified notion of regularization, which is strictly connected with the parsimony principle, and includes many solved exercises that are classified according to the Donald Knuth ranking of difficulty, which essentially consists of a mix of warm-up exercises that lead to deeper research problems. A software simulator is also included. - Presents fundamental machine learning concepts, such as neural networks and kernel machines in a unified manner - Provides in-depth coverage of unsupervised and semi-supervised learning - Includes a software simulator for kernel machines and learning from constraints that also includes exercises to facilitate learning - Contains 250 solved examples and exercises chosen particularly for their progression of difficulty from simple to complex

DNA testing and its forensic analysis are recognized as the “gold standard” in forensic identification science methods. However, there is a great need for a hands-on step-by-step guide to teach the forensic DNA community how to interpret DNA mixtures, how to assign a likelihood ratio, and how to use the subsequent likelihood ratio when reporting interpretation conclusions. Forensic DNA Profiling: A Practical Guide to Assigning Likelihood Ratios will provide a roadmap for labs all over the world and the next generation of analysts who need this foundational understanding. The techniques used in forensic DNA analysis are based upon the accepted principles of molecular biology. The interpretation of a good-quality DNA profile generated from a crime scene stain from a single-source donor provides an unambiguous result when using the most modern forensic DNA methods. Unfortunately, many crime scene profiles are not single source. They are described as mixed since they contain DNA from two or more individuals. Interpretation of DNA mixtures represents one of the greatest challenges to the forensic DNA analyst. As such, the book introduces terms used to describe DNA profiles and profile interpretation. Chapters explain DNA extraction methods, the polymerase chain reaction (PCR), capillary electrophoresis (CE), likelihood ratios (LRs) and their interpretation, and population genetic models—including Mendelian inheritance and Hardy-Weinberg equilibrium. It is important that analysts understand how LRs are generated in a probabilistic framework, ideally with an appreciation of both semicontinuous and fully continuous probabilistic approaches. KEY FEATURES: • The first book to focus entirely on DNA mixtures and the complexities involved with interpreting the results • Takes a hands-on approach offering theory with worked examples and exercises to be easily understood and implementable by laboratory personnel • New methods, heretofore unpublished previously, provide a means to innovate deconvoluting a mixed DNA profile, assign an LR, and appropriately report the weight of evidence • Includes a chapter on assigning LRs for close relatives (i.e., “It’s not me, it was my brother”), and discusses strategies for the validation of probabilistic genotyping software Forensic DNA Profiling fills the void for labs unfamiliar with LRs, and moving to probabilistic solutions, and for labs already familiar with LRs, but wishing to understand how they are calculated in more detail. The book will be a welcome read for lab professionals and technicians, students, and legal professionals seeking to understand and apply the techniques covered.

Charged-Particle Reaction List 1948-1971 is a guide to experiments on charged-particle-induced reactions that have been reported in journal literature during the period 1948 to June 1971. This compendium consists of the material from four Reaction Lists which have already appeared in Nuclear Data Tables. Each published article is listed under the target nuclei in the nuclear reactions which it treats. Reactions are denoted by A(a,b)B, where A and B are the target and residual nucleus, respectively; a is the bombarding charged particle and b is the outgoing product particle or particles. The guide also includes a brief information after the reaction designation, namely, the energy E of the bombarding projectile in MeV, a short statement of the type of data that is found in the paper, and a bibliographic information on the paper itself. A symbol THY in the extreme right-hand column denotes the theoretical papers concerned with analysis of nuclear reaction data. For papers dealing with experimental data on energy spectra, the angle of observation of the emerging reaction products, the accelerator, as well as the detector used are given for many entries under the column heading "Quantity Measured." The guide will prove immensely useful for theoretical physicists, nuclear physicists, and molecular physicists.

Somewhat revised/expanded new edition of a problem-oriented introductory undergraduate text, the first edition of which appeared about a decade ago. The author writes with courteous clarity, and imposes only modest demands upon the mathematical skills of her readers. Problems at the end of each of t