Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory.The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.

This volume explains recent theoretical developments in the econometric modelling of relationships between different statistical series. The statistical techniques explored analyse relationships between different variables, over time, such as the relationship between variables in a macroeconomy. Examples from Professor Terasvirta's empirical work are given. Professors Granger and Terasvirta are leading exponents of techniques of dynamic, multivariate analysis. They illustrate in this volume exploratory ways of using such techniques to provide models of nonlinear relationships between variables. This is an extension of previous work on linear relationships, and on univariate models. These developments will be of use to econometricians wishing to construct and use models of nonlinear, dynamic, multivariate relationships, such as an investment function, or a production function. Particular attention is paid to the case of a single dependent variable modelled by a few explanatory variables and the lagged dependent variable in nonlinear form. The book concentrates on stochastic series, since the existence of unexpected shocks strongly suggests that economic variables are stochastic. Granger and Terasvirta also discuss the division of these nonlinear relationships into parametric and nonparametric models.

This book, and its companion volume in the Econometric Society Monographs series (ESM number 32), present a collection of papers by Clive W. J. Granger. His contributions to economics and econometrics, many of them seminal, span more than four decades and touch on all aspects of time series analysis. The papers assembled in this volume explore topics in causality, integration and cointegration, and long memory. Those in the companion volume investigate themes in causality, integration and cointegration, and long memory. The two volumes contain the original articles as well as an introduction written by the editors.

The History of Econometric Ideas covers the period from the late nineteenth century to the middle of the twentieth century, illustrating how economists first learned to harness statistical methods to measure and test the "laws" of economics. Though scholarly, Dr. Morgan's book is very accessible; it does not require a high level of prior statistical knowledge, and will be of interest to practicing statisticians and economists.

Large observational studies involving research questions that require the measurement of several features on each individual arise in many fields including the social and medical sciences. This book sets out both the general concepts and the more technical statistical issues involved in analysis and interpretation. Numerous illustrative examples are described in outline and four studies are discussed in some detail. The use of graphical representations of dependencies and independencies among the features under study is stressed, both to incorporate available knowledge at the planning stage of an analysis and to summarize aspects important for interpretation after detailed statistical analysis is complete. This book is aimed at research workers using statistical methods as well as statisticians involved in empirical research.

In this definitive book, D. R. Cox gives a comprehensive and balanced appraisal of statistical inference. He develops the key concepts, describing and comparing the main ideas and controversies over foundational issues that have been keenly argued for more than two-hundred years. Continuing a sixty-year career of major contributions to statistical thought, no one is better placed to give this much-needed account of the field. An appendix gives a more personal assessment of the merits of different ideas. The content ranges from the traditional to the contemporary. While specific applications are not treated, the book is strongly motivated by applications across the sciences and associated technologies. The mathematics is kept as elementary as feasible, though previous knowledge of statistics is assumed. The book will be valued by every user or student of statistics who is serious about understanding the uncertainty inherent in conclusions from statistical analyses.

This second edition of David-Hillel Ruben's influential and highly acclaimed book on the philosophy of explanation has been revised and expanded, and the author has made substantial changes in light of the extensive reviews the first edition received. Ruben's views on the place of laws in explanation has been refined and clarified. What is perhaps the central thesis of the book, his realist view of explanation, describing the way in which explanation depends on metaphysics, has been updated and extended and engages with some of the work in this area published since the book's first edition.

A lively and informal introduction to the role of uncertainty and probability in people's lives from an everyday perspective
From television game shows and gambling techniques to weather forecasting and the financial markets, virtually every aspect of modern life involves situations in which the outcomes are uncertain and of varying qualities. But as noted statistician Dennis Lindley writes in this distinctive text, "We want you to face up to uncertainty, not hide it away under false concepts, but to understand it and, moreover, to use the recent discoveries so that you can act in the face of uncertainty more sensibly than would have been possible without the skill."
Accessibly written at an elementary level, this outstanding text examines uncertainty in various everyday situations and introduces readers to three rules--craftily laid out in the book--that prove uncertainty can be handled with as much confidence as ordinary logic. Combining a concept of utility with probability, the book insightfully demonstrates how uncertainty can be measured and used in everyday life, especially in decision-making and science.
With a focus on understanding and using probability calculations, Understanding Uncertainty demystifies probability and:
* Explains in straightforward detail the logic of uncertainty, its truths, and its falsehoods
* Explores what has been learned in the twentieth century about uncertainty
* Provides a logical, sensible method for acting in the face of uncertainty
* Presents vignettes of great discoveries made in the twentieth century
* Shows readers how to discern if another person--whether a lawyer, politician, scientist, or journalist--is talking sense, posing the right questions, or obtaining sound answers
Requiring only a basic understanding of mathematical concepts and operations, Understanding Uncertainty is useful as a text for all students who have probability or statistics as part of their course, even at the most introductory level.

Bayesian nets are widely used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. But many philosophers have criticised and ultimately rejected the central assumption on which such work is based - the Causal Markov Condition. So should Bayesian nets be abandoned? What explains their success in artificial intelligence? This book argues that the Causal Markov Condition holds as a default rule: it often holds but may need to be repealed in the face of counterexamples. Thus Bayesian nets are the right tool to use by default but naively applying them can lead to problems. The book develops a systematic account of causal reasoning and shows how Bayesian nets can be coherently employed to automate the reasoning processes of an artificial agent. The resulting framework for causal reasoning involves not only new algorithms but also new conceptual foundations. Probability and causality are treated as mental notions - part of an agent's belief state.Yet probability and causality are also objective - different agents with the same background knowledge ought to adopt the same or similar probabilistic and causal beliefs. This book, aimed at researchers and graduate students in computer science, mathematics and philosophy, provides a general introduction to these philosophical views as well as an exposition of the computational techniques that they motivate.

Winner of the 2011 Man Booker Prize
By an acclaimed writer at the height of his powers, The Sense of an Ending extends a streak of extraordinary books that began with the best-selling Arthur & George and continued with Nothing to Be Frightened Of and, most recently, Pulse.
This intense new novel follows a middle-aged man as he contends with a past he has never much thought about—until his closest childhood friends return with a vengeance, one of them from the grave, another maddeningly present. Tony Webster thought he’d left all this behind as he built a life for himself, and by now his marriage and family and career have fallen into an amicable divorce and retirement. But he is then presented with a mysterious legacy that obliges him to reconsider a variety of things he thought he’d understood all along, and to revise his estimation of his own nature and place in the world.
A novel so compelling that it begs to be read in a single sitting, with stunning psychological and emotional depth and sophistication, The Sense of an Ending is a brilliant new chapter in Julian Barnes’s oeuvre.

Learning birdsong is not just a way to become a better bird-spotter. It is tuning in: a way of hearing the soundtrack of the planet earth...Why do birds sing? What are they trying to say? Birdsong is not just about natural history. It is also about our history. We got melody from the birds as we got rhythm from the womb. Birds are our music: they teach us to express emotion and beauty in sound. The first instruments ever made were bird-flutes. This book takes the reader on a journey from winter into spring. Along the way, you will learn something of the science of birdsong - the difference between song and call, the physiology of songbirds, what birdsong tells us about evolution, and indeed the very beginnings of life itself. The aim is to give you a flying start in birdsong so that, after reading this book, you'll be listening to order, not chaos, to Bach, not white noise. You will be more aware of the wild world, and better able to understand it.

The design of trading algorithms requires sophisticated mathematical models backed up by reliable data. In this textbook, the authors develop models for algorithmic trading in contexts such as executing large orders, market making, targeting VWAP and other schedules, trading pairs or collection of assets, and executing in dark pools. These models are grounded on how the exchanges work, whether the algorithm is trading with better informed traders (adverse selection), and the type of information available to market participants at both ultra-high and low frequency. Algorithmic and High-Frequency Trading is the first book that combines sophisticated mathematical modelling, empirical facts and financial economics, taking the reader from basic ideas to cutting-edge research and practice. If you need to understand how modern electronic markets operate, what information provides a trading edge, and how other market

In 1630s' Holland thousands of people, from the wealthiest merchants to the lowest street traders, were caught up in a frenzy of buying and selling. The object of the speculation was not oil or gold, but the tulip, a delicate and exotic bloom that had just arrived from the east. Over three years, rare tulip bulbs changed hands for sums that would have bought a house in Amsterdam: a single bulb could sell for more than 300,000 at today's prices. Fortunes were made overnight, but then lost when, within a year, the market collapsed. Mike Dash recreates this bizarre episode in European history, separating myth from reality. He traces the hysterical boom and devastating bust, bringing to life a colourful cast of characters, and beautifully evoking Holland's Golden Age.