A Bayesian Decision Perspective
Author: Franco Taroni,Silvia Bozza,Alex Biedermann,Paolo Garbolino,Colin Aitken
Publisher: John Wiley & Sons
This is the first text to examine the use of statistical methods in forensic science and bayesian statistics in combination. The book is split into two parts: Part One concentrates on the philosophies of statistical inference. Chapter One examines the differences between the frequentist, the likelihood and the Bayesian perspectives, before Chapter Two explores the Bayesian decision-theoretic perspective further, and looks at the benefits it carries. Part Two then introduces the reader to the practical aspects involved: the application, interpretation, summary and presentation of data analyses are all examined from a Bayesian decision-theoretic perspective. A wide range of statistical methods, essential in the analysis of forensic scientific data is explored. These include the comparison of allele proportions in populations, the comparison of means, the choice of sampling size, and the discrimination of items of evidence of unknown origin into predefined populations. Throughout this practical appraisal there are a wide variety of examples taken from the routine work of forensic scientists. These applications are demonstrated in the ever-more popular R language. The reader is taken through these applied examples in a step-by-step approach, discussing the methods at each stage.
Author: Todd D. Little
Publisher: Oxford University Press, USA
This two-volume handbook on current best-practices in quantitative methods as practiced in the social, behavioral, and educational sciences covers philosophical and ethical issues, theory construction, model building and types of models, survey and experiment design, measurement issues, observational methods, statistical methods, types of analysis, types of data, and common research fallacies.
Author: Todd D. Little
Publisher: Oxford University Press
Research today demands the application of sophisticated and powerful research tools. Fulfilling this need, The Oxford Handbook of Quantitative Methods is the complete tool box to deliver the most valid and generalizable answers to todays complex research questions. It is a one-stop source for learning and reviewing current best-practices in quantitative methods as practiced in the social, behavioral, and educational sciences. Comprising two volumes, this handbook covers a wealth of topics related to quantitative research methods. It begins with essential philosophical and ethical issues related to science and quantitative research. It then addresses core measurement topics before delving into the design of studies. Principal issues related to modern estimation and mathematical modeling are also detailed. Topics in the handbook then segway into the realm of statistical inference and modeling with chapters dedicated to classical approaches as well as modern latent variable approaches. Numerous chapters associated with longitudinal data and more specialized techniques round out this broad selection of topics. Comprehensive, authoritative, and user-friendly, this two-volume set will be an indispensable resource for serious researchers across the social, behavioral, and educational sciences.
Author: Raphaël Mourad
Publisher: OUP Oxford
Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity. These data will only allow insights into this wealth of so-called 'omics' data if represented by flexible and scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent a powerful formalism to discover complex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics in the broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest. A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the design of advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes: (1) Gene network inference (2) Causality discovery (3) Association genetics (4) Epigenetics (5) Detection of copy number variations (6) Prediction of outcomes from high-dimensional genomic data. Written by leading international experts, this is a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerful techniques.
Author: Guang-Zhong Yang
The last decade has witnessed a rapid surge of interest in new sensing and monitoring devices for wellbeing and healthcare. One key development in this area is wireless, wearable and implantable in vivo monitoring and intervention. A myriad of platforms are now available from both academic institutions and commercial organisations. They permit the management of patients with both acute and chronic symptoms, including diabetes, cardiovascular diseases, treatment of epilepsy and other debilitating neurological disorders. Despite extensive developments in sensing technologies, there are significant research issues related to system integration, sensor miniaturisation, low-power sensor interface, wireless telemetry and signal processing. In the 2nd edition of this popular and authoritative reference on Body Sensor Networks (BSN), major topics related to the latest technological developments and potential clinical applications are discussed, with contents covering. Biosensor Design, Interfacing and Nanotechnology Wireless Communication and Network Topologies Communication Protocols and Standards Energy Harvesting and Power Delivery Ultra-low Power Bio-inspired Processing Multi-sensor Fusion and Context Aware Sensing Autonomic Sensing Wearable, Ingestible Sensor Integration and Exemplar Applications System Integration and Wireless Sensor Microsystems The book also provides a comprehensive review of the current wireless sensor development platforms and a step-by-step guide to developing your own BSN applications through the use of the BSN development kit.
Deciphering How the Brain Codes Our Thoughts
Author: Stanislas Dehaene
WINNER OF THE 2014 BRAIN PRIZE From the acclaimed author of Reading in the Brain, a breathtaking look at the new science that can track consciousness deep in the brain How does our brain generate a conscious thought? And why does so much of our knowledge remain unconscious? Thanks to clever psychological and brain-imaging experiments, scientists are closer to cracking this mystery than ever before. In this lively book, Stanislas Dehaene describes the pioneering work his lab and the labs of other cognitive neuroscientists worldwide have accomplished in defining, testing, and explaining the brain events behind a conscious state. We can now pin down the neurons that fire when a person reports becoming aware of a piece of information and understand the crucial role unconscious computations play in how we make decisions. The emerging theory enables a test of consciousness in animals, babies, and those with severe brain injuries. A joyous exploration of the mind and its thrilling complexities, Consciousness and the Brain will excite anyone interested in cutting-edge science and technology and the vast philosophical, personal, and ethical implications of finally quantifying consciousness. From the Trade Paperback edition.
A Scaling Approach
Author: Richard M. Sibly,James H. Brown,Astrid Kodric-Brown
Publisher: John Wiley & Sons
One of the first textbooks in this emerging important field of ecology. Most of ecology is about metabolism: the ways that organisms use energy and materials. The energy requirements of individuals – their metabolic rates – vary predictably with their body size and temperature. Ecological interactions are exchanges of energy and materials between organisms and their environments. So metabolic rate affects ecological processes at all levels: individuals, populations, communities and ecosystems. Each chapter focuses on a different process, level of organization, or kind of organism. It lays a conceptual foundation and presents empirical examples. Together, the chapters provide an integrated framework that holds the promise for a unified theory of ecology. The book is intended to be accessible to upper-level undergraduate, and graduate students, but also of interest to senior scientists. Its easy-to-read chapters and clear illustrations can be used in lecture and seminar courses. Together they make for an authoritative treatment that will inspire future generations to study metabolic ecology.
Paths, Dangers, Strategies
Author: Nick Bostrom
Publisher: OUP Oxford
The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but we have cleverer brains. If machine brains one day come to surpass human brains in general intelligence, then this new superintelligence could become very powerful. As the fate of the gorillas now depends more on us humans than on the gorillas themselves, so the fate of our species then would come to depend on the actions of the machine superintelligence. But we have one advantage: we get to make the first move. Will it be possible to construct a seed AI or otherwise to engineer initial conditions so as to make an intelligence explosion survivable? How could one achieve a controlled detonation? To get closer to an answer to this question, we must make our way through a fascinating landscape of topics and considerations. Read the book and learn about oracles, genies, singletons; about boxing methods, tripwires, and mind crime; about humanity's cosmic endowment and differential technological development; indirect normativity, instrumental convergence, whole brain emulation and technology couplings; Malthusian economics and dystopian evolution; artificial intelligence, and biological cognitive enhancement, and collective intelligence. This profoundly ambitious and original book picks its way carefully through a vast tract of forbiddingly difficult intellectual terrain. Yet the writing is so lucid that it somehow makes it all seem easy. After an utterly engrossing journey that takes us to the frontiers of thinking about the human condition and the future of intelligent life, we find in Nick Bostrom's work nothing less than a reconceptualization of the essential task of our time.
Proceedings : 29 November-2 December, 2001, San Jose, California
Author: Nick Cercone,Tsau Y. Lin,Xindong Wu
This proceedings of the November 2001 conference explores the design, analysis and implementation of data mining theory and systems. The 72 regular papers and 37 posters discuss data mining algorithms, data and knowledge representation, modeling of data to support data mining, scalability issues, st
Genomes, Fossils, and Trees
Author: Maximilian J. Telford,D. Timothy J. Littlewood
Publisher: Oxford University Press
Describing and understanding the evolution of the diversity of bodyplans is a major goal of evolutionary biology. Taking a modern, integrated approach to this question, a group of leading researchers describe how modern techniques and disciplines have been combined, resulting in a dramatic renaissance in the study of animal evolution.
Author: Stanislas Dehaene
Wir sind umgeben von Zahlen. Ob auf Kreditkarten gestanzt oder auf Münzen geprägt, ob auf Schecks gedruckt oder in den Spalten computerisierter Tabellen aufgelistet, überall beherrschen Zahlen unser Leben. Sie sind auch der Kern unserer Technologie. Ohne Zahlen könnten wir weder Raketen starten, die das Sonnensystem erkunden, noch Brücken bauen, Güter austauschen oder Rech nungen bezahlen. In gewissem Sinn sind Zahlen also kulturelle Erfindungen, die sich ihrer Bedeutung nach nur mit der Landwirtschaft oder mit dem Rad vergleichen lassen. Aber sie könnten sogar noch tiefere Wurzeln haben. Tausende von Jahren vor Christus benutzten babylonische Wissenschaftler Zahlzeichen, um erstaun lich genaueastronomische Tabellen zu berechnen. Zehntausende von Jahren zuvor hatten Menschen der Steinzeit die ersten geschriebenen Zahlenreihen geschaffen, indem sie Knochen einkerbten oder Punkte auf Höhlenwände malten. Und, wie ich später überzeugend darzustellen hoffe, schon vor weiteren Millionen von Jahren, lange bevor es Menschen gab, nahmen Tiere aller Arten Zahlen zur Kenntnis und stellten mit ihnen einfache Kopfrechnungen an. Sind Zahlen also fast so alt wie das Leben selbst? Sind sie in der Struktur unseres Gehirns verankert? Besitzen wir einen Zahlensinn, eine spezielle Intuition, die uns hilft, Zahlen und Mathematik mit Sinn zu erfüllen? Ich wurde vor fünfzehn Jahren, während meiner Ausbildung zum Mathema tiker, fasziniert von den abstrakten Objekten, mit denen ich umzugehen lernte, vor allem von den einfachsten von ihnen- den Zahlen.
15th International Conference, ALT 2004, Padova, Italy, October 2-5, 2004. Proceedings
Author: Shai Ben David,John Case,Akira Maruoka
Publisher: Springer Science & Business Media
Algorithmic learning theory is mathematics about computer programs which learn from experience. This involves considerable interaction between various mathematical disciplines including theory of computation, statistics, and c- binatorics. There is also considerable interaction with the practical, empirical ?elds of machine and statistical learning in which a principal aim is to predict, from past data about phenomena, useful features of future data from the same phenomena. The papers in this volume cover a broad range of topics of current research in the ?eld of algorithmic learning theory. We have divided the 29 technical, contributed papers in this volume into eight categories (corresponding to eight sessions) re?ecting this broad range. The categories featured are Inductive Inf- ence, Approximate Optimization Algorithms, Online Sequence Prediction, S- tistical Analysis of Unlabeled Data, PAC Learning & Boosting, Statistical - pervisedLearning,LogicBasedLearning,andQuery&ReinforcementLearning. Below we give a brief overview of the ?eld, placing each of these topics in the general context of the ?eld. Formal models of automated learning re?ect various facets of the wide range of activities that can be viewed as learning. A ?rst dichotomy is between viewing learning as an inde?nite process and viewing it as a ?nite activity with a de?ned termination. Inductive Inference models focus on inde?nite learning processes, requiring only eventual success of the learner to converge to a satisfactory conclusion.
Theory, Models, and Data
Author: Li Zhaoping
Publisher: OUP Oxford
While the field of vision science has grown significantly in the past three decades, there have been few comprehensive books that showed readers how to adopt a computional approach to understanding visual perception, along with the underlying mechanisms in the brain. Understanding Vision explains the computational principles and models of biological visual processing, and in particular, of primate vision. The book is written in such a way that vision scientists, unfamiliar with mathematical details, should be able to conceptually follow the theoretical principles and their relationship with physiological, anatomical, and psychological observations, without going through the more mathematical pages. For those with a physical science background, especially those from machine vision, this book serves as an analytical introduction to biological vision. It can be used as a textbook or a reference book in a vision course, or a computational neuroscience course for graduate students or advanced undergraduate students. It is also suitable for self-learning by motivated readers. in addition, for those with a focused interest in just one of the topics in the book, it is feasible to read just the chapter on this topic without having read or fully comprehended the other chapters. In particular, Chapter 2 presents a brief overview of experimental observations on biological vision; Chapter 3 is on encoding of visual inputs, Chapter 5 is on visual attentional selection driven by sensory inputs, and Chapter 6 is on visual perception or decoding. Including many examples that clearly illustrate the application of computational principles to experimental observations, Understanding Vision is valuable for students and researchers in computational neuroscience, vision science, machine and computer vision, as well as physicists interested in visual processes.
Author: David Gibson
Publisher: OUP Oxford
The field of plant population ecology has advanced considerably in the last decade since the first edition was published. In particular there have been substantial and ongoing advances in statistics and modelling applications in population ecology, as well as an explosion of new techniques reflecting the availability of new technologies (e.g. affordable and accurate Global Positioning Systems) and advances in molecular biology. This new edition has been updated and revised with more recent examples replacing older ones where appropriate. The book's trademark question-driven approach has been maintained and some important topics such as the metapopulation concept which are missing entirely from the current edition are now included throughout the text.