This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Since the first edition only five years ago, the simulation world has changed significantly -- current techniques have matured and new ones have appeared. This new edition deals with these new developments; in particular, there are sections on:Transition path sampling and diffusive barrier crossing to simulaterare eventsDissipative particle dynamic as a course-grained simulation techniqueNovel schemes to compute the long-ranged forcesHamiltonian and non-Hamiltonian dynamics in the context constant-temperature and constant-pressure molecular dynamics simulationsMultiple-time step algorithms as an alternative for constraintsDefects in solidsThe pruned-enriched Rosenbluth sampling, recoil-growth, and concerted rotations for complex moleculesParallel tempering for glassy Hamiltonians
Examples are included that highlight current applications and the codes of case studies are available on the World Wide Web. Several new examples have been added since the first edition to illustrate recent applications. Questions are included in this new edition. No prior knowledge of computer simulation is assumed.
Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.
You’ll learn how to:Wrangle—transform your datasets into a form convenient for analysisProgram—learn powerful R tools for solving data problems with greater clarity and easeExplore—examine your data, generate hypotheses, and quickly test themModel—provide a low-dimensional summary that captures true "signals" in your datasetCommunicate—learn R Markdown for integrating prose, code, and results
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
This book will help you:Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification
Corresponding data sets are available at www.wiley.com/go/9781118876138.
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