Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

The capabilities of modern technology are rapidly increasing, spurred on to a large extent by the tremendous advances in communications and computing. Automated vehicles and global wireless connections are some examples of these advances. In order to take advantage of such enhanced capabilities, our need to model and manipulate our knowledge of the geophysical world, using compatible representations, is also rapidly increasing. In response to this one fundamental issue of great concern in modern geographical research is how to most effectively capture the physical world around us in systems like geographical information systems (GIS). Making this task even more challenging is the fact that uncertainty plays a pervasive role in the representation, analysis and use of geospatial information. The types of uncertainty that appear in geospatial information systems are not the just simple randomness of observation, as in weather data, but are manifested in many other forms including imprecision, incompleteness and granularization. Describing the uncertainty of the boundaries of deserts and mountains clearly require different tools than those provided by probability theory. The multiplicity of modalities of uncertainty appearing in GIS requires a variety of formalisms to model these uncertainties. In light of this it is natural that fuzzy set theory has become a topic of intensive interest in many areas of geographical research and applications This volume, Fuzzy Modeling with Spatial Information for Geographic Problems, provides many stimulating examples of advances in geographical research based on approaches using fuzzy sets and related technologies.

Any brain activity relies on the interaction of thousands of neurons, each of which integrating signals from thousands of synapses. While neurons are undoubtedly the building blocks of the brain, synapses constitute the main loci of information transfer that lead to the emergence of neuronal code. Investigating synaptic transmission constitutes a multi-faceted challenge that brings together a large number of techniques and expertise ranging from experimental to computational approaches, bringing together paradigms spanning from molecular to neural network level. In this book, we have collected a series of articles that present foundational work aimed at shedding much-needed light on brain information processing, synaptic transmission and neural code formation. Some articles present analyses of regulatory mechanisms underlying neural code formation and its elaboration at the molecular level, while others use computational and modelling approaches to investigate, at synaptic, neuronal and inter-neuronal level, how the different mechanisms involved in information processing interact to generate effects like long-term potentiation (LTP), which constitutes the cellular basis of learning and memory. This collection, although not exhaustive, aims to present a framework of the most used investigational paradigms and showcase results that may, in turn, generate novel hypotheses and ideas for further studies and investigations.

An accessible introduction to performing meta-analysis across various areas of research The practice of meta-analysis allows researchers to obtain findings from various studies and compile them to verify and form one overall conclusion. Statistical Meta-Analysis with Applications presents the necessary statistical methodologies that allow readers to tackle the four main stages of meta-analysis: problem formulation, data collection, data evaluation, and data analysis and interpretation. Combining the authors' expertise on the topic with a wealth of up-to-date information, this book successfully introduces the essential statistical practices for making thorough and accurate discoveries across a wide array of diverse fields, such as business, public health, biostatistics, and environmental studies. Two main types of statistical analysis serve as the foundation of the methods and techniques: combining tests of effect size and combining estimates of effect size. Additional topics covered include: Meta-analysis regression procedures Multiple-endpoint and multiple-treatment studies The Bayesian approach to meta-analysis Publication bias Vote counting procedures Methods for combining individual tests and combining individual estimates Using meta-analysis to analyze binary and ordinal categorical data Numerous worked-out examples in each chapter provide the reader with a step-by-step understanding of the presented methods. All exercises can be computed using the R and SAS software packages, which are both available via the book's related Web site. Extensive references are also included, outlining additional sources for further study. Requiring only a working knowledge of statistics, Statistical Meta-Analysis with Applications is a valuable supplement for courses in biostatistics, business, public health, and social research at the upper-undergraduate and graduate levels. It is also an excellent reference for applied statisticians working in industry, academia, and government.

This fully updated and revised new edition provides a comprehensive, state-of-the art review of this field, and will serve as a valuable resource for clinicians, surgeons and researchers with an interest in prostate cancer. The book reviews new data about molecular characteristics of the disease, profiles the new grading system for prostate cancer introduced in 2015, and provides new perspectives about imaging of prostate cancer, as well as the role of targeted biopsies. The text summarizes the role of biomarkers and MRI in patient selection and management and details the world wide results of active surveillance. Specific chapters address communication and ethical issues, QOL outcomes, economic aspects, and psycho-social aspects of surveillance. The role of focal therapy for low risk disease is summarized, and the data supporting preventive interventions during surveillance reviewed. This text will serve as a very useful resource for physicians and researchers dealing with, and interested in this common malignancy, as it provides a concise yet comprehensive summary of the current status of the field that will help guide patient management and stimulate investigative efforts.