Tony and Cyrus have taken to the road, exploring the British Isles and adding their own spicy twist to our most classic and best-loved dishes. Try jazzing up a Sunday roast chicken with a honey and ginger, adding a cumin and coriander kick to a shepherd's pie or lacing a Victoria sponge with aromatic fennel seeds and cardamom.With delicious, everyday recipes accompanied by Cyrus and Tony's top tips and favourite spices, The Incredible Spice Men will demystify the contents of your spice rack, and open your everyday cooking up to a world of exciting new flavours.

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    Hundreds of thousands of Python developers around the world rely on Python for general-purpose tasks, Internet scripting, systems programming, user interfaces, and product customization. Available on all major computing platforms, including commercial versions of Unix, Linux, Windows, and Mac OS X, Python is portable, powerful and remarkable easy to use.With its convenient, quick-reference format, "Python Pocket Reference," 3rd Edition is the perfect on-the-job reference. More importantly, it's now been refreshed to cover the language's latest release, Python 2.4. For experienced Python developers, this book is a compact toolbox that delivers need-to-know information at the flip of a page. This third edition also includes an easy-lookup index to help developers find answers fast!Python 2.4 is more than just optimization and library enhancements; it's also chock full of bug fixes and upgrades. And these changes are addressed in the "Python Pocket Reference," 3rd Edition. New language features, new and upgraded built-ins, and new and upgraded modules and packages--they're all clarified in detail.The "Python Pocket Reference," 3rd Edition serves as the perfect companion to "Learning Python" and "Programming Python."

    Theoretical, yet applied. Statistics for Business and Economics, Eleventh Edition, gives you the best of both worlds. Using a rich array of applications from a variety of industries, McClave/Sincich/Benson clearly demonstrates how to use statistics effectively in a business environment.The book focuses on developing statistical thinking so the reader can better assess the credibility and value of inferences made from data. As consumers and future producers of statistical inferences, readers are introduced to a wide variety of data collection and analysis techniques to help them evaluate data and make informed business decisions. As with previous editions, this revision offers an abundance of applications with many new and updated exercises that draw on real business situations and recent economic events. The authors assume a background of basic algebra.

    The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process.Create vectors, handle variables, and perform other basic functionsInput and output dataTackle data structures such as matrices, lists, factors, and data framesWork with probability, probability distributions, and random variablesCalculate statistics and confidence intervals, and perform statistical testsCreate a variety of graphic displaysBuild statistical models with linear regressions and analysis of variance (ANOVA)Explore advanced statistical techniques, such as finding clusters in your dataWonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language--one practical example at a time.--Jeffrey Ryan, software consultant and R package author

    This report provides a brief yet comprehensive introduction to Python for engineers, researchers, and data scientists who are already familiar with another programming language.Author Jake VanderPlas, an interdisciplinary research director at the University of Washington, explains Python’s essential syntax and semantics, built-in data types and structures, function definitions, control flow statements, and more, using Python 3 syntax.You’ll explore:- Python syntax basics and running Python codeBasic semantics of Python variables, objects, and operators- Built-in simple types and data structures- Control flow statements for executing code blocks conditionally- Methods for creating and using reusable functionsIterators, list comprehensions, and generators- String manipulation and regular expressions- Python’s standard library and third-party modules- Python’s core data science tools- Recommended resources to help you learn more

    Marvel at Chichen Itza's massive El Castilo pyramid, swim in a cenote, and party in a steamy Caribbean cantina in Merida; all with your trusted travel companion. Get to the heart of Cancun, Cozumel and the Yucatan and begin your journey now! Inside Lonely Planet Cancun, Cozumel & the Yucatan Travel Guide: Color maps and images throughout Highlights and itineraries help you tailor your trip to your personal needs and interests Insider tips to save time and money and get around like a local, avoiding crowds and trouble spots Essential info at your fingertips - hours of operation, phone numbers, websites, transit tips, prices Honest reviews for all budgets - eating, sleeping, sight-seeing, going out, shopping, hidden gems that most guidebooks miss Cultural insights give you a richer, more rewarding travel experience - cuisine, history, art, literature, Maya heritage, landscapes, wildlife, customs and etiquette Over 30 maps Covers Cancun, Isla Mujeres, Isla Cozumel, Riviera Maya, Costa Maya and the Southern Caribbean Coast, Yucatan State, Chichen Itza, Campeche, Chiapas and more eBook Features: (Best viewed on tablet devices and smartphones) Downloadable PDF and offline maps prevent roaming and data charges Effortlessly navigate and jump between maps and reviews Add notes to personalise your guidebook experience Seamlessly flip between pages Bookmarks and speedy search capabilities get you to key pages in a flash Embedded links to recommendations' websites Zoom-in maps and images Inbuilt dictionary for quick referencing The Perfect Choice: Lonely Planet Cancun, Cozumel & the Yucatan, our most comprehensive guide to Cancun, Cozumel and the Yucatan, is perfect for both exploring top sights and taking roads less traveled. Looking for more extensive coverage? Check out Lonely Planet Mexico for a comprehensive look at all the country has to offer. Authors: Written and researched by Lonely Planet, John Hecht, Lucas Vidgen. About Lonely Planet: Since 1973, Lonely Planet has become the world's leading travel media company with guidebooks to every destination, an award-winning website, mobile and digital travel products, and a dedicated traveler community. Lonely Planet covers must-see spots but also enables curious travelers to get off beaten paths to understand more of the culture of the places in which they find themselves.

    Fundamentals Of Mathematical Statistics is written by SC Gupta and VK Kapoor and published by SULTAN CHAND & SONS, Delhi.

    No longer. A new generation of tools and massive amounts of available data make it easy for anyone to create visualizations that communicate ideas far more effectively than generic spreadsheet charts ever could.What’s more, building good charts is quickly becoming a need-to-have skill for managers. If you’re not doing it, other managers are, and they’re getting noticed for it and getting credit for contributing to your company’s success.In Good Charts, dataviz maven Scott Berinato provides an essential guide to how visualization works and how to use this new language to impress and persuade. Dataviz today is where spreadsheets and word processors were in the early 1980s—on the cusp of changing how we work. Berinato lays out a system for thinking visually and building better charts through a process of talking, sketching, and prototyping.This book is much more than a set of static rules for making visualizations. It taps into both well-established and cutting-edge research in visual perception and neuroscience, as well as the emerging field of visualization science, to explore why good charts (and bad ones) create “feelings behind our eyes.” Along the way, Berinato also includes many engaging vignettes of dataviz pros, illustrating the ideas in practice.Good Charts will help you turn plain, uninspiring charts that merely present information into smart, effective visualizations that powerfully convey ideas.

    Prepare yourself for a distinctively Russian cauldron of artistry and history, nightclubs and vibrant street life. Mara Vorhees, Lonely Planet Writer Our PromiseYou can trust our travel information because Lonely Planet authors visit the places we write about, each and every edition. We never accept freebies for positive coverage so you can rely on us to tell it like it is.Inside This Book 80 art galleries & museums59 bars and cafes reviewed28 kremlins, cathedrals & palaces11 revamped exhibitions & art spaces2 banyas to get naked & sweat inComprehensive map sectionFeature coverage of top sightsRange of planning toolsIn-depth background on Russian art, architecture & literature

    Offering an approach with a visual/graphical emphasis, this text offers a number of examples on the premise that students learn best by doing. This book features an emphasis on interpretation of results and critical thinking over calculations.

    Fundamentals of Biostatistics with CD-Rom.

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

    The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo MCMC. Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.