Sam Faulkner travels back in time to medieval Scotland, ancient Egypt, and Renaissance Bruges in search of his missing father.

Jack Finney's beloved sequel to his classic, New York Times bestselling illustrated novel Time and Again. Simon Morley, whose logic-defying trip to the New York City of the 1880s in Time and Again has enchanted readers for twenty-five years, embarks on another trip across the borders of time. This time Reuben Prien at the secret, government-sponsored Project wants Si to leave his home in the 1880s and visit New York in 1912. Si's mission: to protect a man who is traveling across the Atlantic with vital documents that could avert World War I. So one fateful day in 1912, Si finds himself aboard the world's most famous ship...the Titanic.

NEWBERY MEDAL WINNER • TIME MAGAZINE’S 100 BEST FANTASY BOOKS OF ALL TIME • NOW A MAJOR MOTION PICTURE FROM DISNEY Read the ground-breaking science fiction and fantasy classic that has delighted children for over 60 years! "A Wrinkle in Time is one of my favorite books of all time. I've read it so often, I know it by heart." —Meg Cabot Late one night, three otherworldly creatures appear and sweep Meg Murry, her brother Charles Wallace, and their friend Calvin O'Keefe away on a mission to save Mr. Murray, who has gone missing while doing top-secret work for the government. They travel via tesseract--a wrinkle that transports one across space and time--to the planet Camazotz, where Mr. Murray is being held captive. There they discover a dark force that threatens not only Mr. Murray but the safety of the whole universe. A Wrinkle in Time is the first book in Madeleine L’Engle’s Time Quintet.

Some of the key mathematical results are stated without proof in order to make the underlying theory acccessible to a wider audience. The book assumes a knowledge only of basic calculus, matrix algebra, and elementary statistics. The emphasis is on methods and the analysis of data sets. The logic and tools of model-building for stationary and non-stationary time series are developed in detail and numerous exercises, many of which make use of the included computer package, provide the reader with ample opportunity to develop skills in this area. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Additional topics include harmonic regression, the Burg and Hannan-Rissanen algorithms, unit roots, regression with ARMA errors, structural models, the EM algorithm, generalized state-space models with applications to time series of count data, exponential smoothing, the Holt-Winters and ARAR forecasting algorithms, transfer function models and intervention analysis. Brief introducitons are also given to cointegration and to non-linear, continuous-time and long-memory models. The time series package included in the back of the book is a slightly modified version of the package ITSM, published separately as ITSM for Windows, by Springer-Verlag, 1994. It does not handle such large data sets as ITSM for Windows, but like the latter, runs on IBM-PC compatible computers under either DOS or Windows (version 3.1 or later). The programs are all menu-driven so that the reader can immediately apply the techniques in the book to time series data, with a minimal investment of time in the computational and algorithmic aspects of the analysis.

Discover the essential thinking tools you’ve been missing with The Great Mental Models series by Shane Parrish, New York Times bestselling author and the mind behind the acclaimed Farnam Street blog and “The Knowledge Project” podcast. This first book in the series is your guide to learning the crucial thinking tools nobody ever taught you. Time and time again, great thinkers such as Charlie Munger and Warren Buffett have credited their success to mental models–representations of how something works that can scale onto other fields. Mastering a small number of mental models enables you to rapidly grasp new information, identify patterns others miss, and avoid the common mistakes that hold people back. The Great Mental Models: Volume 1, General Thinking Concepts shows you how making a few tiny changes in the way you think can deliver big results. Drawing on examples from history, business, art, and science, this book details nine of the most versatile, all-purpose mental models you can use right away to improve your decision making and productivity. This book will teach you how to: Avoid blind spots when looking at problems. Find non-obvious solutions. Anticipate and achieve desired outcomes. Play to your strengths, avoid your weaknesses, … and more. The Great Mental Models series demystifies once elusive concepts and illuminates rich knowledge that traditional education overlooks. This series is the most comprehensive and accessible guide on using mental models to better understand our world, solve problems, and gain an advantage.

Change of Time and Change of Measure provides a comprehensive account of two topics that are of particular significance in both theoretical and applied stochastics: random change of time and change of probability law.Random change of time is key to understanding the nature of various stochastic processes, and gives rise to interesting mathematical results and insights of importance for the modeling and interpretation of empirically observed dynamic processes. Change of probability law is a technique for solving central questions in mathematical finance, and also has a considerable role in insurance mathematics, large deviation theory, and other fields.The book comprehensively collects and integrates results from a number of scattered sources in the literature and discusses the importance of the results relative to the existing literature, particularly with regard to mathematical finance.In this Second Edition a Chapter 13 entitled 'A Wider View' has been added. This outlines some of the developments that have taken place in the area of Change of Time and Change of Measure since the publication of the First Edition. Most of these developments have their root in the study of the Statistical Theory of Turbulence rather than in Financial Mathematics and Econometrics, and they form part of the new research area termed 'Ambit Stochastics'.