Contains complete worked solutions to all regular exercises and computer exercises in the text; additional test questions and their solutions; an online laboratory manual for the computer algebra system GAP, with exercises tied to the book and an instructor answer key; and links on the author's website to true/false questions, flash cards, essays, software downloads, and other abstract algebra-related materials.

    Republished in book form shortly thereafter, it has since gone through four hardcover and sixteen paperback printings. It is a revolutionary work, astounding in its foresight and contemporaneity. The University of Illinois Press is pleased and honored to issue this commemorative reprinting of a classic.

    

    Fishing, swimming, golf, tennis. Seems like the ideal location for a former secret agent posing as a retired police officer and part-time private investigator. Until people start disappearing. Suggested reading order for the Nicholas Colt series: COLT LADY 52 POCKET-47 CROSSCUT SNUFF TAG 9 KEY DEATH BLOOD TATTOO SYCAMORE BLUFF THE JACK REACHER FILES: FUGITIVE THE JACK REACHER FILES: VELOCITY (Novella) THE BLOOD NOTEBOOKS Note: Although published at a later date, the events in COLT and LADY 52 precede those in Jude Hardin's debut thriller POCKET-47. All of the books listed work as stand-alone thrillers, depending on reader preference. Nicholas Colt also appears in several short stories, including the one titled RATTLED and the one titled RACKED. Praise for Jude Hardin’s Thrillers: POCKET-47 sucked me in and held me enthralled. Author Jude Hardin keeps the pace frantic, the thrills non-stop, but best of all is his hero, the wonderfully ironic Nicholas Colt. This is a character I'm eager to follow through many adventures to come. —Tess Gerritsen, New York Times bestselling author of ICE COLD. The best PI debut I've read in years, fit to share shelf space with the best of Ross Macdonald, Sue Grafton, and Robert B. Parker. POCKET-47 is so hot you may burn your hands reading. Highly recommended. —J.A. Konrath, author of the Jack Daniels mysteries Hardin gets everything right in his powerhouse thriller debut, which introduces rock star–turned–PI Nicholas Colt. —Publishers Weekly on POCKET-47 KEY DEATH is an exhilarating thriller that punches way above its weight. It hits you hard and fast with crackling suspense, hair-raising twists and stunning revelations. Word of advice: don't start on this one unless you're prepared to stay up all night. —John Ling, author of THE BLASPHEMER Colt is a physical, no-holds-barred PI, reminiscent of Robert B. Parker's Spenser and Lee Child's Jack Reacher, and his debut is action-packed. With a hefty toll of dead bodies, some described in cringe-inducing detail, this is crime fiction at its rawest. Hard-boiled connoisseurs should make Colt's acquaintance now. —Booklist on POCKET-47 With CROSSCUT, Jude Hardin takes the PI novel and psychological suspense to a new, unrestrained level. Fast, fierce, and relentless. —David Morrell, New York Times bestselling creator of Rambo

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    The clarity and eloquence of the presentation make it popular with teachers and students alike. The text aims to expand the reader's view of the field and to present standard material in a novel way. All of the most important topics in the field are covered with a fresh perspective, including iterative methods for systems of equations and eigenvalue problems and the underlying principles of conditioning and stability. Presentation is in the form of 40 lectures, which each focus on one or two central ideas. The unity between topics is emphasized throughout, with no risk of getting lost in details and technicalities. The book breaks with tradition by beginning with the QR factorization - an important and fresh idea for students, and the thread that connects most of the algorithms of numerical linear algebra.

    This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results.The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.-- "Journal of Economics"

    Python is easy to learn, and it's perfect for exploring topics like statistics, geometry, probability, and calculus. You’ll learn to write programs to find derivatives, solve equations graphically, manipulate algebraic expressions, even examine projectile motion.Rather than crank through tedious calculations by hand, you'll learn how to use Python functions and modules to handle the number crunching while you focus on the principles behind the math. Exercises throughout teach fundamental programming concepts, like using functions, handling user input, and reading and manipulating data. As you learn to think computationally, you'll discover new ways to explore and think about math, and gain valuable programming skills that you can use to continue your study of math and computer science.If you’re interested in math but have yet to dip into programming, you’ll find that Python makes it easy to go deeper into the subject—let Python handle the tedious work while you spend more time on the math.

    Ian Hacking here presents a philosophical critique of early ideas about probability, induction and statistical inference and the growth of this new family of ideas in the fifteenth, sixteenth and seventeenth centuries. The contemporary debate centres round such figures as Pascal, Leibniz and Jacques Bernoulli. What brought about the change in ideas? The author invokes in his explanation a wider intellectual framework involving the growth of science, economics and the theology of the period.

    This revised edition includes a CD-Rom with exercises that will help the student have a better understanding of equations, formulas, etc.

    This overview has enabled students to make sense more easily of what instructors are doing when they produce proofs, theorems and formulas.

    No statistical knowledge is required, and your programming skills can range from hobbyist to pro.Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to: Create artful graphs to visualize complex data sets and functions Write more efficient code using parallel R and vectorization Interface R with C/C++ and Python for increased speed or functionality Find new R packages for text analysis, image manipulation, and more Squash annoying bugs with advanced debugging techniques Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.