The World's Wild Places' is an incredible collection of panoramic photographs by award-winning photographer Colin Prior. The breathtaking scenery captured by Colin is from many different countries of the world, including locations in South and Western Australia and New Zealand. In this powerful collection of work, he presents an unforgettable record of the great wild places of the earth, particularly significant as many are under environmental threat.
This book provides the most comprehensive treatment to date of microeconometrics, the analysis of individual-level data on the economic behavior of individuals or firms using regression methods for cross section and panel data. The book is oriented to the practitioner. A basic understanding of the linear regression model with matrix algebra is assumed. The text can be used for a microeconometrics course, typically a second-year economics PhD course; for data-oriented applied microeconometrics field courses; and as a reference work for graduate students and applied researchers who wish to fill in gaps in their toolkit. Distinguishing features of the book include emphasis on nonlinear models and robust inference, simulation-based estimation, and problems of complex survey data. The book makes frequent use of numerical examples based on generated data to illustrate the key models and methods. More substantially, it systematically integrates into the text empirical illustrations based on seven large and exceptionally rich data sets.
The first edition of Statistics and the Evaluation of Evidence for Forensic Scientists established itself as a highly regarded authority on this area. Fully revised and updated, the second edition provides significant new material on areas of current interest including: Glass Interpretation Fibres Interpretation Bayes’ Nets The title presents comprehensive coverage of the statistical evaluation of forensic evidence. It is written with the assumption of a modest mathematical background and is illustrated throughout with up-to-date examples from a forensic science background. The clarity of exposition makes this book ideal for all forensic scientists, lawyers and other professionals in related fields interested in the quantitative assessment and evaluation of evidence. 'There can be no doubt that the appreciation of some evidence in a court of law has been greatly enhanced by the sound use of statistical ideas and one can be confident that the next decade will see further developments, during which time this book will admirably serve those who have cause to use statistics in forensic science.' D.V. Lindley
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.
This volume offers a solution to one of the central, unsolved problems of Western philosophy, that of induction. It explores the implications of Hume's argument that successful prediction tells us nothing about the truth of the predicting theory.
Knowledge and Reality brings together a selection of Colin McGinn's philosophical essays from the 1970s to the 1990s, whose unifying theme is the relation between the mind and the world. The essays range over a set of prominent topics in contemporary philosophy, including the analysis of knowledge, the a priori, necessity, possible worlds, realism, mental representation, appearance and reality, and colour. McGinn has written a new postscript to each essay, placing it in its philosophical context by sketching the background against which it was written, explaining its relations to other notable work, and offering his current reflections on the topic. The volume thus traces the development of McGinn's ideas and their role in some central philosophical debates. Seen together the essays offer a many-sided defence of realism, while emphasizing the epistemological price that realism exacts.
In this clearly reasoned defense of Bayes's Theorem -- that probability can be used to reasonably justify scientific theories -- Colin Howson and Peter Urbach examine the way in which scientists appeal to probability arguments, and demonstrate that the classical approach to statistical inference is full of flaws. Arguing the case for the Bayesian method with little more than basic algebra, the authors show that it avoids the difficulties of the classical system. The book also refutes the major criticisms leveled against Bayesian logic, especially that it is too subjective. This newly updated edition of this classic textbook is also suitable for college courses.
Small businesses in virtually all industrialized countries find it increasingly difficult to obtain finance from institutional sources. Banks have become more risk-averse; venture capital funds, previously of only marginal significance, are now often concentrating their investments on established companies; and management buyouts and buyins and pressures to reduce government spending have resulted in a reduction in public policy initiatives. In this context there is a growing interest in the role of the informal venture capital market as an alternative source of risk finance for small business. Informal Venture Capital: Investors, Investments and Policy Issues in Finland investigates the phenomenon of `business angels' - wealthy private individuals who invest in small businesses - who are increasingly recognized throughout the developed world as representing the most important source of venture capital for entrepreneurial businesses in their start-up and early growth stages. This volume answers key questions about these investors, and contributes significant new evidence on aspects of the informal venture capital market which have not been examined in previous studies. It further provides an authoritative assessment of the effectiveness of policy initiatives to stimulate the supply of informal venture capital, based on the experiences in Finland.
In this book the authors describe the principles and methods behind probabilistic forecasting and Bayesian data assimilation. Instead of focusing on particular application areas, the authors adopt a general dynamical systems approach, with a profusion of low-dimensional, discrete-time numerical examples designed to build intuition about the subject. Part I explains the mathematical framework of ensemble-based probabilistic forecasting and uncertainty quantification. Part II is devoted to Bayesian filtering algorithms, from classical data assimilation algorithms such as the Kalman filter, variational techniques, and sequential Monte Carlo methods, through to more recent developments such as the ensemble Kalman filter and ensemble transform filters. The McKean approach to sequential filtering in combination with coupling of measures serves as a unifying mathematical framework throughout Part II. Assuming only some basic familiarity with probability, this book is an ideal introduction for graduate students in applied mathematics, computer science, engineering, geoscience and other emerging application areas.
How do you obtain permission? How can you satisfactorily tackle objections? How can you convince planning officers of the value of your work? Drawing on substantial experience from both applicant and local planning authority perspectives, this book provides tactics and practical steps to help architects secure early validation of applications and successful outcomes. It’s a practical guide to understanding the planning system and maximizing the potential for successful outcomes. Readers will develop a greater understanding of the principles that are vital in the preparation and negotiation of applications against the very complex detail of regulatory arrangements.
Mathematics of Planet Earth (MPE) was started and continues to be consolidated as a collaboration of mathematical science organisations around the world. These organisations work together to tackle global environmental, social and economic problems using mathematics.This textbook introduces the fundamental topics of MPE to advanced undergraduate and graduate students in mathematics, physics and engineering while explaining their modern usages and operational connections. In particular, it discusses the links between partial differential equations, data assimilation, dynamical systems, mathematical modelling and numerical simulations and applies them to insightful examples.The text also complements advanced courses in geophysical fluid dynamics (GFD) for meteorology, atmospheric science and oceanography. It links the fundamental scientific topics of GFD with their potential usage in applications of climate change and weather variability. The immediacy of examples provides an excellent introduction for experienced researchers interested in learning the scope and primary concepts of MPE.
International Bestseller: The essential guidebook to the history of magic and occultism—“the most interesting, informative, and thought-provoking book on [the occult]” (The Sunday Telegraph) Colin Wilson’s great classic work is a comprehensive history of mystery and magic. His genius lies in producing a skillful synthesis of the available material; clarifying without simplifying, seeing the occult in the light of reason and reason in the light of the mystical and paranormal. It is a journey of enlightenment—a wide-ranging survey of the whole subject and an insightful exploration of Man’s latent powers. Republished two years after the author’s death, and with a new foreword by bibliographer Colin Stanley, Wilson brings his own refreshingly optimistic and stimulating interpretation to the worlds of the paranormal, the occult, and the supernatural.
Bayesian Networks “This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation.” Dr. Ian Evett, Principal Forensic Services Ltd, London, UK Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science Second Edition Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates diffculties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader’s own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.
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