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.

    Beginning millions of years ago with ancient “ant odometers” and moving through time to our modern-day quest for new dimensions, it covers 250 milestones in mathematical history. Among the numerous delights readers will learn about as they dip into this inviting anthology: cicada-generated prime numbers, magic squares from centuries ago, the discovery of pi and calculus, and the butterfly effect. Each topic gets a lavishly illustrated spread with stunning color art, along with formulas and concepts, fascinating facts about scientists’ lives, and real-world applications of the theorems.

    In this collection of landmark mathematical works, editor Stephen Hawking has assembled the greatest feats humans have ever accomplished using just numbers and their brains.

    It bridges the gap between the mechanics of econometrics and modern applications of econometrics by employing a systematic approach motivated by the major problems facing applied researchers today. Throughout the text, the emphasis on examples gives a concrete reality to economic relationships and allows treatment of interesting policy questions in a realistic and accessible framework.

    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.

     Speed Mathematics teaches simple methods that will enable you to make lightning calculations in your head-including multiplication, division, addition, and subtraction, as well as working with fractions, squaring numbers, and extracting square and cube roots. Here's just one example of this revolutionary approach to basic mathematics: 96 x 97 = Subtract each number from 100. 96 x 97 = 4 3 Subtract diagonally. Either 96--3 or 97-- 4. The result is the first part of the answer. 96 x 97 = 93 4 3 Multiply the numbers in the circles. 4 x 3 = 12. This is the second part of the answer. 96 x 97 = 9312 4 3 It's that easy!

    Buy a plant, dig a hole in the ground, drop the plant in it, and cover it up waiting for blooms to suddenly appear. If only it were that simple. The overall idea of gardening is basic, but gardening is very intricate. It

    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.

    One of the chapters in each trivia book was “Disturbing Facts about Our World”. I decided to fill an entire volume with facts about upsetting crimes and mayhem, combined with unbelievable yet real instances of misfortune and misery. This is a book where grim examples of bigotry and hypocrisy are intertwined with amusing stories of bad luck. In the spirit of the times we live in, I dedicated a whole chapter to COVID-19 trivia and weird medical conditions. I am well aware that many potential readers might be overwhelmed by the condensed negativity, but hey… a fact is a fact! Continue to read if you are curious to learn:- Why were the trousers of New Zealand’s farmers exploding?- What is the depressing origin of the phrase “Hip Hip, Hooray”?- Why did the Spanish Habsburgs royal family sleep with human mummies?- Why was it legal in Iceland until 2015 to kill Basque people?- Who was the “Deep Throat” informer from the Watergate scandal?- How many people were killed trying to cross the notorious Berlin Wall?- Why do snakes make a better pet than cats or dogs?- How can millipedes cause a train crash?- What is the etymology of “thug”?- What are the chances of getting killed by rubbish falling from space?- How did polygamist men in Kuwait manage to visit all their wives during the coronavirus lockdown?However incredible these pieces of trivia might sound, all entries have been verified and fact-checked.

    This hands-on guide shows you how to maintain a successful bar, manage the business aspect of it, and stake your place in your town's nightlife. It provides informative tips on:Understanding the business and laws of owning a bar Developing a business plan Creating a menu, choosing decor, and establishing a theme Stocking up on equipment Choosing and dealing with employees Handling tough customers Controlling expenses, managing inventory, and controlling cash flow Getting the word out about your place Preparing for your grand opening, step-by-step This guide cues you in on how to keep your bar safe and clean, making sure everyone is having fun. It warns you about the pitfalls and no-nos that every owner should avoid. There are also helpful resources, such as contact information for State Alcohol Control Boards and Web sites with valuable information.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    To understand algebra is to possess the power to grow your skills and knowledge so you can ace your courses and possibly pursue further study in math. Algebra II For Dummies is the fun and easy way to get a handle on this subject and solve even the trickiest algebra problems. This friendly guide shows you how to get up to speed on exponential functions, laws of logarithms, conic sections, matrices, and other advanced algebra concepts. In no time you'll have the tools you need to:Interpret quadratic functions Find the roots of a polynomial Reason with rational functions Expose exponential and logarithmic functions Cut up conic sections Solve linear and non linear systems of equations Equate inequalities Simplifyy complex numbers Make moves with matrices Sort out sequences and sets This straightforward guide offers plenty of multiplication tricks that only math teachers know. It also profiles special types of numbers, making it easy for you to categorize them and solve any problems without breaking a sweat. When it comes to understanding and working out algebraic equations, Algebra II For Dummies is all you need to succeed!

    From 'swilling and swirling' to 'Syrah and Chardonnay', The Bluffer's Guide to Wine contains everything you need to know to pass yourself off as an informed imbiber.

    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.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.