Mathieu Marion offers a careful, historically informed study of Wittgenstein's philosophy of mathematics. This area of his work has frequently been undervalued by Wittgenstein specialists and by philosophers of mathematics alike; but the surprising fact that he wrote more on this subject than on any other indicates its centrality in his thought. Marion traces the development of Wittgenstein's thinking in the context of the mathematical and philosophical work of the times, to make coherent sense of ideas that have too often been misunderstood because they have been presented in a disjointed and incomplete way. In particular, he illuminates the work of the neglected 'transitional period' between the Tractatus and the Investigations. Marion shows that study of Wittgenstein's writings on mathematics is essential to a proper understanding of his philosophy; and he also demonstrates that it has much to contribute to current debates about the foundations of mathematics.

In Decision Making and Rationality in the Modern World, Keith E. Stanovich demonstrates how work in the cognitive psychology of decision making has implications for the large and theoretically contentious debates about the nature of human rationality. Written specifically for undergraduate psychology students, the book presents a very practical approach to decision making, which is too often perceived by students as an artificial set of skills used only in academia and not in the real world. Instead, Stanovich shows how good decision-making procedures support rational behavior that enables people to act most efficiently to fulfill their goals. He explains how the concept of rationality is understood in cognitive science in terms of good decision making and judgment.
Books in the Fundamentals of Cognition series serve as ideal instructional resources for advanced courses in cognitive psychology. They provide an up-to-date, well-organized survey of our current understanding of the major theories of cognitive psychology. The books are concise, which allows instructors to incorporate the latest original research and readings into their courses without overburdening their students. Focused without being too advanced--and comprehensive without being too broad--these books are the perfect resource for both students and instructors.

Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools―robust to input noise and distortion, able to exploit long-range contextual information―that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary.
The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video.
Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.

Theoretical results suggest that in order to learn the kind of complicated
functions that can represent high-level abstractions (e.g., in
vision, language, and other AI-level tasks), one may need deep architectures.
Deep architectures are composed of multiple levels of non-linear
operations, such as in neural nets with many hidden layers or in complicated
propositional formulae re-using many sub-formulae. Searching
the parameter space of deep architectures is a difficult task, but learning
algorithms such as those for Deep Belief Networks have recently been
proposed to tackle this problem with notable success, beating the stateof-
the-art in certain areas. This monograph discusses the motivations
and principles regarding learning algorithms for deep architectures, in
particular those exploiting as building blocks unsupervised learning of
single-layer models such as Restricted Boltzmann Machines, used to
construct deeper models such as Deep Belief Networks.

By some estimates, quantitative (or algorithmic) trading now accounts for over one-third of trading volume in the United States. While institutional traders continue to implement this highly effective approach, many independent traders—with limited resources and less computing power—have wondered if they can still challenge powerful industry professionals at their own game? The answer is "yes," and in Quantitative Trading, author Dr. Ernest Chan, a respected independent trader and consultant, will show you how.
Whether you're an independent "retail" trader looking to start your own quantitative trading business or an individual who aspires to work as a quantitative trader at a major financial institution, this practical guide contains the information you need to succeed.
Organized around the steps you should take to start trading quantitatively, this book skillfully addresses how to:
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Find a viable trading strategy that you're both comfortable with and confident in
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Backtest your strategy—with MATLAB®, Excel, and other platforms—to ensure good historical performance
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Build and implement an automated trading system to execute your strategy
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Scale up or wind down your strategies depending on their real-world profitability
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Manage the money and risks involved in holding positions generated by your strategy
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Incorporate advanced concepts that most professionals use into your everyday trading activities
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And much more
While Dr. Chan takes the time to outline the essential aspects of turning quantitative trading strategies into profits, he doesn't get into overly theoretical or sophisticated theories. Instead, he highlights the simple tools and techniques you can use to gain a much-needed edge over today's institutional traders.
And for those who want to keep up with the latest news, ideas, and trends in quantitative trading, you're welcome to visit Dr. Chan's blog, epchan.blogspot.com, as well as his premium content Web site, epchan.com/subscriptions, which you'll have free access to with purchase of this book.
As an independent trader, you're free from the con-straints found in today's institutional environment—and as long as you adhere to the discipline of quantitative trading, you can achieve significant returns. With this reliable resource as your guide, you'll quickly discover what it takes to make it in such a dynamic and demanding field.

Praise for Algorithmic Trading: "Algorithmic Trading is an insightful book on quantitative trading written by a seasoned practitioner. What sets this book apart from many others in the space is the emphasis on real examples as opposed to just theory. Concepts are not only described, they are brought to life with actual trading strategies, which give the reader insight into how and why each strategy was developed, how it was implemented, and even how it was coded. This book is a valuable resource for anyone looking to create their own systematic trading strategies and those involved in manager selection, where the knowledge contained in this book will lead to a more informed and nuanced conversation with managers." (Daren Smith, CFA, CAIA, FSA, Managing Director, Manager Selection & Portfolio Construction, University of Toronto Asset Management). "Using an excellent selection of mean reversion and momentum strategies, Ernie explains the rationale behind each one, shows how to test it, how to improve it, and discusses implementation issues. His book is a careful, detailed exposition of the scientific method applied to strategy development. For serious retail traders, I know of no other book that provides this range of examples and level of detail. His discussions of how regime changes affect strategies, and of risk management, are invaluable bonuses." (Roger Hunter, Mathematician and Algorithmic Trader).

John Locke's theory of property is perhaps the most distinctive and the most influential aspect of his political theory. In this book James Tully uses an hermeneutical and analytical approach to offer a revolutionary revision of early modern theories of property, focusing particularly on that of Locke. Setting his analysis within the intellectual context of the seventeenth century, Professor Tully overturns the standard interpretations of Locke's theory, showing that it is not a justification of private property. Instead he shows it to be a theory of individual use rights within a framework of inclusive claim rights. He links Locke's conception of rights not merely to his ethical theory, but to the central arguments of his epistemology, and illuminates the way in which Locke's theory is tied to his metaphysical views of God and man, his theory of revolution and his account of a legitimate polity.

A variety of problems in machine learning and digital communication deal with complex but structured natural or artificial systems. In this book, Brendan Frey uses graphical models as an overarching framework to describe and solve problems of pattern classification, unsupervised learning, data compression, and channel coding. Using probabilistic structures such as Bayesian belief networks and Markov random fields, he is able to describe the relationships between random variables in these systems and to apply graph-based inference techniques to develop new algorithms. Among the algorithms described are the wake-sleep algorithm for unsupervised learning, the iterative turbodecoding algorithm (currently the best error-correcting decoding algorithm), the bits-back coding method, the Markov chain Monte Carlo technique, and variational inference.

Graphical models, a marriage between probability theory and graph theory, provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering--uncertainty and complexity. In particular, they play an increasingly important role in the design and analysis of machine learning algorithms. Fundamental to the idea of a graphical model is the notion of modularity: a complex system is built by combining simpler parts. Probability theory serves as the glue whereby the parts are combined, ensuring that the system as a whole is consistent and providing ways to interface models to data. Graph theory provides both an intuitively appealing interface by which humans can model highly interacting sets of variables and a data structure that lends itself naturally to the design of efficient general-purpose algorithms.This book presents an in-depth exploration of issues related to learning within the graphical model formalism. Four chapters are tutorial chapters--Robert Cowell on Inference for Bayesian Networks, David MacKay on Monte Carlo Methods, Michael I. Jordan et al. on Variational Methods, and David Heckerman on Learning with Bayesian Networks. The remaining chapters cover a wide range of topics of current research interest.

NATIONAL BOOK CRITICS CIRCLE AWARD WINNER
This groundbreaking book offers the first global history of the loyalist exodus to Canada, the Caribbean, Sierra Leone, India, and beyond.
At the end of the American Revolution, sixty thousand Americans loyal to the British cause fled the United States and became refugees throughout the British Empire. Liberty’s Exiles tells their story. This surprising new account of the founding of the United States and the shaping of the post-revolutionary world traces extraordinary journeys like the one of Elizabeth Johnston, a young mother from Georgia, who led her growing family to Britain, Jamaica, and Canada, questing for a home; black loyalists such as David George, who escaped from slavery in Virginia and went on to found Baptist congregations in Nova Scotia and Sierra Leone; and Mohawk Indian leader Joseph Brant, who tried to find autonomy for his people in Ontario. Ambitious, original, and personality-filled, this book is at once an intimate narrative history and a provocative analysis that changes how we see the revolution’s “losers” and their legacies.

For over thirty years, Nobel Prize winner David H. Hubel has been at the forefront of research on questions of vision. In Eye, Brain, and Vision, he brings you to the edge of current knowledge about vision, and explores the tasks scientists face in deciphering the many remaining mysteries of vision and the workings of the human brain.

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

This popular text provides an accessible guide to the application, interpretation, and pitfalls of structural equation modeling (SEM). Reviewed are fundamental statistical concepts--such as correlation, regressions, data preparation and screening, path analysis, and confirmatory factor analysis--as well as more advanced methods, including the evaluation of nonlinear effects, measurement models and structural regression models, latent growth models, and multilevel SEM. Special features include a Web page offering data and program syntax files for many of the research examples, electronic overheads that can be downloaded and printed by instructors or students, and links to SEM-related resources.