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| introduction to probability charles m grinstead: Introduction to Probability David F. Anderson, Timo Seppäläinen, Benedek Valkó, 2017-11-02 This classroom-tested textbook is an introduction to probability theory, with the right balance between mathematical precision, probabilistic intuition, and concrete applications. Introduction to Probability covers the material precisely, while avoiding excessive technical details. After introducing the basic vocabulary of randomness, including events, probabilities, and random variables, the text offers the reader a first glimpse of the major theorems of the subject: the law of large numbers and the central limit theorem. The important probability distributions are introduced organically as they arise from applications. The discrete and continuous sides of probability are treated together to emphasize their similarities. Intended for students with a calculus background, the text teaches not only the nuts and bolts of probability theory and how to solve specific problems, but also why the methods of solution work. |
| introduction to probability charles m grinstead: Probability Tales Charles Miller Grinstead, William Paul Peterson, James Laurie Snell, 2011 This book explores four real-world topics through the lens of probability theory. It can be used to supplement a standard text in probability or statistics. Most elementary textbooks present the basic theory and then illustrate the ideas with some neatly packaged examples. Here the authors assume that the reader has seen, or is learning, the basic theory from another book and concentrate in some depth on the following topics: streaks, the stock market, lotteries, and fingerprints. This extended format allows the authors to present multiple approaches to problems and to pursue promising side discussions in ways that would not be possible in a book constrained to cover a fixed set of topics. To keep the main narrative accessible, the authors have placed the more technical mathematical details in appendices. The appendices can be understood by someone who has taken one or two semesters of calculus. |
| introduction to probability charles m grinstead: Introduction to Probability \ Charles M. Grinstead, J. Laurie Snell, 1997 |
| introduction to probability charles m grinstead: Introduction to Probability Charles Miller Grinstead, James Laurie Snell, 1997 This text is designed for an introductory probability course at the university level for undergraduates in mathematics, the physical and social sciences, engineering, and computer science. It presents a thorough treatment of probability ideas and techniques necessary for a firm understanding of the subject. |
| introduction to probability charles m grinstead: Introduction to Probability, Second Edition Joseph K. Blitzstein, Jessica Hwang, 2019-02-08 Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and toolsfor understanding statistics, randomness, and uncertainty. 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 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. The second edition adds many new examples, exercises, and explanations, to deepen understanding of the ideas, clarify subtle concepts, and respond to feedback from many students and readers. New supplementary online resources have been developed, including animations and interactive visualizations, and the book has been updated to dovetail with these resources. Supplementary material is available on Joseph Blitzstein’s website www. stat110.net. The supplements include: Solutions to selected exercises Additional practice problems Handouts including review material and sample exams Animations and interactive visualizations created in connection with the edX online version of Stat 110. Links to lecture videos available on ITunes U and YouTube There is also a complete instructor's solutions manual available to instructors who require the book for a course. |
| introduction to probability charles m grinstead: Introduction to GNU Octave Jason Lachniet, 2018-11-21 A brief introduction to scientific computing with GNU Octave. Designed as a textbook supplement for freshman and sophomore level linear algebra and calculus students. |
| introduction to probability charles m grinstead: The Drunkard's Walk Leonard Mlodinow, 2008-05-13 NATIONAL BESTSELLER • From the classroom to the courtroom and from financial markets to supermarkets, an intriguing and illuminating look at how randomness, chance, and probability affect our daily lives that will intrigue, awe, and inspire. “Mlodinow writes in a breezy style, interspersing probabilistic mind-benders with portraits of theorists.... The result is a readable crash course in randomness.” —The New York Times Book Review With the born storyteller's command of narrative and imaginative approach, Leonard Mlodinow vividly demonstrates how our lives are profoundly informed by chance and randomness and how everything from wine ratings and corporate success to school grades and political polls are less reliable than we believe. By showing us the true nature of chance and revealing the psychological illusions that cause us to misjudge the world around us, Mlodinow gives us the tools we need to make more informed decisions. From the classroom to the courtroom and from financial markets to supermarkets, Mlodinow's intriguing and illuminating look at how randomness, chance, and probability affect our daily lives will intrigue, awe, and inspire. |
| introduction to probability charles m grinstead: Introduction to Probability David F. Anderson, Timo Seppäläinen, Benedek Valkó, 2017-11-02 This classroom-tested textbook is an introduction to probability theory, with the right balance between mathematical precision, probabilistic intuition, and concrete applications. Introduction to Probability covers the material precisely, while avoiding excessive technical details. After introducing the basic vocabulary of randomness, including events, probabilities, and random variables, the text offers the reader a first glimpse of the major theorems of the subject: the law of large numbers and the central limit theorem. The important probability distributions are introduced organically as they arise from applications. The discrete and continuous sides of probability are treated together to emphasize their similarities. Intended for students with a calculus background, the text teaches not only the nuts and bolts of probability theory and how to solve specific problems, but also why the methods of solution work. |
| introduction to probability charles m grinstead: Building State Capability Matt Andrews, Lant Pritchett, Michael J. V. Woolcock, 2017 Governments play a major role in the development process, and constantly introduce reforms and policies to achieve developmental objectives. Many of these interventions have limited impact, however; schools get built but children don't learn, IT systems are introduced but not used, plans are written but not implemented. These achievement deficiencies reveal gaps in capabilities, and weaknesses in the process of building state capability. This book addresses these weaknesses and gaps. It starts by providing evidence of the capability shortfalls that currently exist in many countries, showing that many governments lack basic capacities even after decades of reforms and capacity building efforts. The book then analyses this evidence, identifying capability traps that hold many governments back - particularly related to isomorphic mimicry (where governments copy best practice solutions from other countries that make them look more capable even if they are not more capable) and premature load bearing (where governments adopt new mechanisms that they cannot actually make work, given weak extant capacities). The book then describes a process that governments can use to escape these capability traps. Called PDIA (problem driven iterative adaptation), this process empowers people working in governments to find and fit solutions to the problems they face. The discussion about this process is structured in a practical manner so that readers can actually apply tools and ideas to the capability challenges they face in their own contexts. These applications will help readers devise policies and reforms that have more impact than those of the past. |
| introduction to probability charles m grinstead: How to Lie with Statistics Darrell Huff, 2010-12-07 If you want to outsmart a crook, learn his tricks—Darrell Huff explains exactly how in the classic How to Lie with Statistics. From distorted graphs and biased samples to misleading averages, there are countless statistical dodges that lend cover to anyone with an ax to grind or a product to sell. With abundant examples and illustrations, Darrell Huff’s lively and engaging primer clarifies the basic principles of statistics and explains how they’re used to present information in honest and not-so-honest ways. Now even more indispensable in our data-driven world than it was when first published, How to Lie with Statistics is the book that generations of readers have relied on to keep from being fooled. |
| introduction to probability charles m grinstead: Programming Challenges Steven S Skiena, Miguel A. Revilla, 2006-04-18 There are many distinct pleasures associated with computer programming. Craftsmanship has its quiet rewards, the satisfaction that comes from building a useful object and making it work. Excitement arrives with the flash of insight that cracks a previously intractable problem. The spiritual quest for elegance can turn the hacker into an artist. There are pleasures in parsimony, in squeezing the last drop of performance out of clever algorithms and tight coding. The games, puzzles, and challenges of problems from international programming competitions are a great way to experience these pleasures while improving your algorithmic and coding skills. This book contains over 100 problems that have appeared in previous programming contests, along with discussions of the theory and ideas necessary to attack them. Instant online grading for all of these problems is available from two WWW robot judging sites. Combining this book with a judge gives an exciting new way to challenge and improve your programming skills. This book can be used for self-study, for teaching innovative courses in algorithms and programming, and in training for international competition. The problems in this book have been selected from over 1,000 programming problems at the Universidad de Valladolid online judge. The judge has ruled on well over one million submissions from 27,000 registered users around the world to date. We have taken only the best of the best, the most fun, exciting, and interesting problems available. |
| introduction to probability charles m grinstead: Probability for Statistics and Machine Learning Anirban DasGupta, 2011-05-17 This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning. It is written in an extremely accessible style, with elaborate motivating discussions and numerous worked out examples and exercises. The book has 20 chapters on a wide range of topics, 423 worked out examples, and 808 exercises. It is unique in its unification of probability and statistics, its coverage and its superb exercise sets, detailed bibliography, and in its substantive treatment of many topics of current importance. This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability. |
| introduction to probability charles m grinstead: Asymptopia Joel Spencer, 2014-06-24 Asymptotics in one form or another are part of the landscape for every mathematician. The objective of this book is to present the ideas of how to approach asymptotic problems that arise in discrete mathematics, analysis of algorithms, and number theory. A broad range of topics is covered, including distribution of prime integers, Erdős Magic, random graphs, Ramsey numbers, and asymptotic geometry. The author is a disciple of Paul Erdős, who taught him about Asymptopia. Primes less than , graphs with vertices, random walks of steps--Erdős was fascinated by the limiting behavior as the variables approached, but never reached, infinity. Asymptotics is very much an art. The various functions , , , , all have distinct personalities. Erdős knew these functions as personal friends. It is the author's hope that these insights may be passed on, that the reader may similarly feel which function has the right temperament for a given task. This book is aimed at strong undergraduates, though it is also suitable for particularly good high school students or for graduates wanting to learn some basic techniques. Asymptopia is a beautiful world. Enjoy! |
| introduction to probability charles m grinstead: Probability and Random Processes S. Palaniammal, 2011-06-30 Presents the fundamental concepts and applications of probability and random processes. Beginning with a discussion of probability theory, the text analyses various types of random processes. It also discusses in detail the random variables, standard distributions, correlation and spectral densities, and linear systems. |
| introduction to probability charles m grinstead: Probability Theory , 2013 Probability theory |
| introduction to probability charles m grinstead: Cardano Øystein Ore, 2017-03-14 Cardano, next to Vesalius the greatest physician of his day, was also a devoted and skilled gambler who played for personal pleasure and profit. His mathematical genius enabled him to devise simple rules of probability for his own benefit and for his gambling contemporaries. These he collected in his Book on Games of Chance and embellished them with essays on the tricks of cheats and kibitzers, as well as on psychological rules of play. In this biography of a stormy Renaissance personality, Cardano's gambling studies are deciphered for the first time, and a translation of the Book on Games of Chance is appended. Originally published in 1953. The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905. |
| introduction to probability charles m grinstead: An Introduction to Probability Theory and Its Applications William Feller, 1968 · Introduction: The Nature of Probability Theory· The Sample Space· Elements of Combinatorial Analysis· Fluctuations in Coin Tossing and Random Walks· Combination of Events· Conditional Probability· Stochastic Independence· The Binomial and Poisson Distributions· The Normal Approximation to the Binomial Distribution· Unlimited Sequences of Bernoulli Trials· Random Variables· Expectation· Laws of Large Numbers· Integral Valued Variables· Generating Functions· Compound Distributions· Branching Processes· Recurrent Events· Renewal Theory· Random Walk and Ruin Problems· Markov Chains· Algebraic Treatment of Finite Markov Chains· The Simplest Time-Dependent Stochastic Processes |
| introduction to probability charles m grinstead: Introduction to Probability with Statistical Applications Géza Schay, 2016-06-17 Now in its second edition, this textbook serves as an introduction to probability and statistics for non-mathematics majors who do not need the exhaustive detail and mathematical depth provided in more comprehensive treatments of the subject. The presentation covers the mathematical laws of random phenomena, including discrete and continuous random variables, expectation and variance, and common probability distributions such as the binomial, Poisson, and normal distributions. More classical examples such as Montmort's problem, the ballot problem, and Bertrand’s paradox are now included, along with applications such as the Maxwell-Boltzmann and Bose-Einstein distributions in physics. Key features in new edition: * 35 new exercises * Expanded section on the algebra of sets * Expanded chapters on probabilities to include more classical examples * New section on regression * Online instructors' manual containing solutions to all exercises“/p> Advanced undergraduate and graduate students in computer science, engineering, and other natural and social sciences with only a basic background in calculus will benefit from this introductory text balancing theory with applications. Review of the first edition: This textbook is a classical and well-written introduction to probability theory and statistics. ... the book is written ‘for an audience such as computer science students, whose mathematical background is not very strong and who do not need the detail and mathematical depth of similar books written for mathematics or statistics majors.’ ... Each new concept is clearly explained and is followed by many detailed examples. ... numerous examples of calculations are given and proofs are well-detailed. (Sophie Lemaire, Mathematical Reviews, Issue 2008 m) |
| introduction to probability charles m grinstead: Fifty Challenging Problems in Probability with Solutions Frederick Mosteller, 2012-04-26 Remarkable puzzlers, graded in difficulty, illustrate elementary and advanced aspects of probability. These problems were selected for originality, general interest, or because they demonstrate valuable techniques. Also includes detailed solutions. |
| introduction to probability charles m grinstead: Random Walks and Electric Networks Peter G. Doyle, J. Laurie Snell , 1984-12-31 Probability theory, like much of mathematics, is indebted to physics as a source of problems and intuition for solving these problems. Unfortunately, the level of abstraction of current mathematics often makes it difficult for anyone but an expert to appreciate this fact. Random Walks and electric networks looks at the interplay of physics and mathematics in terms of an example—the relation between elementary electric network theory and random walks —where the mathematics involved is at the college level. |
| introduction to probability charles m grinstead: Time Travel and Other Mathematical Bewilderments Martin Gardner, 2020-10-06 Martin Gardner's Mathematical Games columns in Scientific American inspired and entertained several generations of mathematicians and scientists. Gardner in his crystal-clear prose illuminated corners of mathematics, especially recreational mathematics, that most people had no idea existed. His playful spirit and inquisitive nature invite the reader into an exploration of beautiful mathematical ideas along with him. These columns were both a revelation and a gift when he wrote them; no one--before Gardner--had written about mathematics like this. They continue to be a marvel. This is the original 1988 edition and contains columns published from 1974-1976. |
| introduction to probability charles m grinstead: Business Statistics Demystified Steven M. Kemp, Sid Kemp, 2004-07-02 Say goodbye to dry presentations, grueling formulas, and abstract theories that would put Einstein to sleep -- now there's an easier way to master the disciplines you really need to know. McGraw-Hill's Demystified Series teaches complex subjects in a unique, easy-to-absorb manner, and is perfect for users without formal training or unlimited time. They're also the most time-efficient, interestingly written brush-ups you can find. Organized as self-teaching guides, they come complete with key points, background information, questions at the end of each chapter, and even final exams. You'll be able to learn more in less time, evaluate your areas of strength and weakness and reinforce your knowledge and confidence. This self-teaching guide brings business statistics down to an understandable level, using practical examples. Coverage includes: probability, analysis of variance, designed experiments, preparing statistical reports, basic statistical procedures, and much more. |
| introduction to probability charles m grinstead: Introduction to Probability for Data Science Stanley H. Chan, 2021 Probability is one of the most interesting subjects in electrical engineering and computer science. It bridges our favorite engineering principles to the practical reality, a world that is full of uncertainty. However, because probability is such a mature subject, the undergraduate textbooks alone might fill several rows of shelves in a library. When the literature is so rich, the challenge becomes how one can pierce through to the insight while diving into the details. For example, many of you have used a normal random variable before, but have you ever wondered where the 'bell shape' comes from? Every probability class will teach you about flipping a coin, but how can 'flipping a coin' ever be useful in machine learning today? Data scientists use the Poisson random variables to model the internet traffic, but where does the gorgeous Poisson equation come from? This book is designed to fill these gaps with knowledge that is essential to all data science students. -- Preface. |
| introduction to probability charles m grinstead: #Introduction to Probability , 2009 |
| introduction to probability charles m grinstead: A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing (Ninth Edition) Burton G. Malkiel, 2007-12-17 Updated with a new chapter that draws on behavioral finance, the field that studies the psychology of investment decisions, the bestselling guide to investing evaluates the full range of financial opportunities. |
| introduction to probability charles m grinstead: One Thousand Exercises in Probability Geoffrey Grimmett, David Stirzaker, 2001-05-24 This guide provides a wide-ranging selection of illuminating, informative and entertaining problems, together with their solution. Topics include modelling and many applications of probability theory. |
| introduction to probability charles m grinstead: Winding Around John Roe, 2015-09-03 The winding number is one of the most basic invariants in topology. It measures the number of times a moving point P goes around a fixed point Q, provided that P travels on a path that never goes through Q and that the final position of P is the same as its starting position. This simple idea has far-reaching applications. The reader of this book will learn how the winding number can help us show that every polynomial equation has a root (the fundamental theorem of algebra),guarantee a fair division of three objects in space by a single planar cut (the ham sandwich theorem),explain why every simple closed curve has an inside and an outside (the Jordan curve theorem),relate calculus to curvature and the singularities of vector fields (the Hopf index theorem),allow one to subtract infinity from infinity and get a finite answer (Toeplitz operators),generalize to give a fundamental and beautiful insight into the topology of matrix groups (the Bott periodicity theorem). All these subjects and more are developed starting only from mathematics that is common in final-year undergraduate courses. |
| introduction to probability charles m grinstead: Bayes' Theorem Examples Dan Morris, 2016-10-02 ***** #1 Kindle Store Bestseller in Mathematics (Throughout 2016) ********** #1 Kindle Store Bestseller in Education Theory (Throughout 2017) *****If you are looking for a short beginners guide packed with visual examples, this book is for you. Bayes' Theorem Examples: A Beginners Visual Approach to Bayesian Data Analysis If you've recently used Google search to find something, Bayes' Theorem was used to find your search results. The same is true for those recommendations on Netflix. Hedge funds? Self-driving cars? Search and Rescue? Bayes' Theorem is used in all of the above and more. At its core, Bayes' Theorem is a simple probability and statistics formula that has revolutionized how we understand and deal with uncertainty. If life is seen as black and white, Bayes' Theorem helps us think about the gray areas. When new evidence comes our way, it helps us update our beliefs and create a new belief.Ready to dig in and visually explore Bayes' Theorem? Let's go! Over 60 hand-drawn visuals are included throughout the book to help you work through each problem as you learn by example. The beautifully hand-drawn visual illustrations are specifically designed and formatted for the kindle.This book also includes sections not found in other books on Bayes' Rule. These include: A short tutorial on how to understand problem scenarios and find P(B), P(A), and P(B|A). - For many people, knowing how to approach scenarios and break them apart can be daunting. In this booklet, we provide a quick step-by-step reference on how to confidently understand scenarios. A few examples of how to think like a Bayesian in everyday life. Bayes' Rule might seem somewhat abstract, but it can be applied to many areas of life and help you make better decisions. Learn how Bayes can help you with critical thinking, problem-solving, and dealing with the gray areas of life. A concise history of Bayes' Rule. - Bayes' Theorem has a fascinating 200+ year history, and we have summed it up for you in this booklet. From its discovery in the 1700's to its being used to break the German's Enigma Code during World War 2. Fascinating real-life stories on how Bayes' formula is used everyday.From search and rescue to spam filtering and driverless cars, Bayes is used in many areas of modern day life. An expanded Bayes' Theorem definition, including notations, and proof section. - In this section we define core elementary bayesian statistics terms more concretely. A recommended readings sectionFrom The Theory That Would Not Die to Think Bayes: Bayesian Statistics in Pythoni> and many more, there are a number of fantastic resources we have collected for further reading. If you are a visual learner and like to learn by example, this intuitive Bayes' Theorem 'for dummies' type book is a good fit for you. Praise for Bayes' Theorem Examples ...What Morris has presented is a useful way to provide the reader with a basic understanding of how to apply the theorem. He takes it easy step by easy step and explains matters in a way that almost anyone can understand. Moreover, by using Venn Diagrams and other visuals, he gives the reader multiple ways of understanding exactly what is going on in Bayes' theorem. The way in which he presents this material helps solidify in the reader's mind how to use Bayes' theorem... - Doug E. - TOP 100 REVIEWER...For those who are predominately Visual Learners, as I certainly am, I highly recommend this book...I believe I gained more from this book than I did from college statistics. Or at least, one fantastic refresher after 20 some years after the fact. - Tin F. TOP 50 REVIEWER |
| introduction to probability charles m grinstead: Dark Data David J. Hand, 2022-02-15 Data describe and represent the world. However, no matter how big they may be, data sets don't - indeed cannot - capture everything. Data are measurements - and, as such, they represent only what has been measured. They don't necessarily capture all the information that is relevant to the questions we may want to ask. If we do not take into account what may be missing/unknown in the data we have, we may find ourselves unwittingly asking questions that our data cannot actually address, come to mistaken conclusions, and make disastrous decisions. In this book, David Hand looks at the ubiquitous phenomenon of missing data. He calls this dark data (making a comparison to dark matter - i.e., matter in the universe that we know is there, but which is invisible to direct measurement). He reveals how we can detect when data is missing, the types of settings in which missing data are likely to be found, and what to do about it. It can arise for many reasons, which themselves may not be obvious - for example, asymmetric information in wars; time delays in financial trading; dropouts in clinical trials; deliberate selection to enhance apparent performance in hospitals, policing, and schools; etc. What becomes clear is that measuring and collecting more and more data (big data) will not necessarily lead us to better understanding or to better decisions. We need to be vigilant to what is missing or unknown in our data, so that we can try to control for it. How do we do that? We can be alert to the causes of dark data, design better data-collection strategies that sidestep some of these causes - and, we can ask better questions of our data, which will lead us to deeper insights and better decisions-- |
| introduction to probability charles m grinstead: Machine Learning in Action Peter Harrington, 2012-04-03 Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce |
| introduction to probability charles m grinstead: Statistics Made Simple K. V. S. Sarma, 2004-08 |
| introduction to probability charles m grinstead: Ramsey Theory on the Integers Bruce M. Landman, Aaron Robertson, 2014-11-10 Ramsey theory is the study of the structure of mathematical objects that is preserved under partitions. In its full generality, Ramsey theory is quite powerful, but can quickly become complicated. By limiting the focus of this book to Ramsey theory applied to the set of integers, the authors have produced a gentle, but meaningful, introduction to an important and enticing branch of modern mathematics. Ramsey Theory on the Integers offers students a glimpse into the world of mathematical research and the opportunity for them to begin pondering unsolved problems. For this new edition, several sections have been added and others have been significantly updated. Among the newly introduced topics are: rainbow Ramsey theory, an inequality version of Schur's theorem, monochromatic solutions of recurrence relations, Ramsey results involving both sums and products, monochromatic sets avoiding certain differences, Ramsey properties for polynomial progressions, generalizations of the Erdős-Ginzberg-Ziv theorem, and the number of arithmetic progressions under arbitrary colorings. Many new results and proofs have been added, most of which were not known when the first edition was published. Furthermore, the book's tables, exercises, lists of open research problems, and bibliography have all been significantly updated. This innovative book also provides the first cohesive study of Ramsey theory on the integers. It contains perhaps the most substantial account of solved and unsolved problems in this blossoming subject. This breakthrough book will engage students, teachers, and researchers alike. |
| introduction to probability charles m grinstead: Markov Chains J. R. Norris, 1998-07-28 Markov chains are central to the understanding of random processes. This is not only because they pervade the applications of random processes, but also because one can calculate explicitly many quantities of interest. This textbook, aimed at advanced undergraduate or MSc students with some background in basic probability theory, focuses on Markov chains and quickly develops a coherent and rigorous theory whilst showing also how actually to apply it. Both discrete-time and continuous-time chains are studied. A distinguishing feature is an introduction to more advanced topics such as martingales and potentials in the established context of Markov chains. There are applications to simulation, economics, optimal control, genetics, queues and many other topics, and exercises and examples drawn both from theory and practice. It will therefore be an ideal text either for elementary courses on random processes or those that are more oriented towards applications. |
| introduction to probability charles m grinstead: No Limit Hold 'em David Sklansky, Ed Miller, 2006 No limit hold 'em is exploding in popularity. Before 2000, it could be difficult to find a game. In 2006, it is played everywhere - in casino cardrooms, in backrooms and homes, and on the Internet. Now anyone can find a game, but few know how to play well. Most players learn by watching television or by listening to dubious advice from their friends. While they may have picked up a valuable tidbit here or there, most players have two options: wise up or go broke. The world's foremost poker theorist, David Sklansky, and noted poker authority, Ed Miller, will wise you up quickly. No Limit Hold 'em: Theory and Practice is the definitive work on this complex game. It provides you a window into the heads of experts, teaching you in straightforward and enjoyable terms the how's and why's of winning play. Book jacket. |
| introduction to probability charles m grinstead: Introduction to Probability and Statistics Using R G. Jay Kerns, 2010-01-10 This is a textbook for an undergraduate course in probability and statistics. The approximate prerequisites are two or three semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors. |
| introduction to probability charles m grinstead: Games, Gods and Gambling Florence Nightingale David, 2012-10-01 Additional Contributors Are Jean Edmiston, E. H. Thorne, And Maxine Merrington. |
| introduction to probability charles m grinstead: The Joy of Factoring Samuel S. Wagstaff (Jr.), 2013-10-24 This book is about the theory and practice of integer factorization presented in a historic perspective. It describes about twenty algorithms for factoring and a dozen other number theory algorithms that support the factoring algorithms. Most algorithms are described both in words and in pseudocode to satisfy both number theorists and computer scientists. Each of the ten chapters begins with a concise summary of its contents. This book is written for readers who want to learn more about the best methods of factoring integers, many reasons for factoring, and some history of this fascinating subject. It can be read by anyone who has taken a first course in number theory. -- Publisher website. |
| introduction to probability charles m grinstead: The Math Book Clifford A. Pickover, 2011-09-27 The Neumann Prize–winning, illustrated exploration of mathematics—from its timeless mysteries to its history of mind-boggling discoveries. Beginning millions of years ago with ancient “ant odometers” and moving through time to our modern-day quest for new dimensions, The Math Book 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 is lavishly illustrated with colorful art, along with formulas and concepts, fascinating facts about scientists’ lives, and real-world applications of the theorems. |
| introduction to probability charles m grinstead: A First Look at Rigorous Probability Theory Jeffrey Seth Rosenthal, 2006 Features an introduction to probability theory using measure theory. This work provides proofs of the essential introductory results and presents the measure theory and mathematical details in terms of intuitive probabilistic concepts, rather than as separate, imposing subjects. |
| introduction to probability charles m grinstead: Stochastic Calculus for Finance I Steven Shreve, 2005-06-28 Developed for the professional Master's program in Computational Finance at Carnegie Mellon, the leading financial engineering program in the U.S. Has been tested in the classroom and revised over a period of several years Exercises conclude every chapter; some of these extend the theory while others are drawn from practical problems in quantitative finance |
INTRODUCTION Definition & Meaning - Merriam-Webster
The meaning of INTRODUCTION is something that introduces. How to use introduction in a sentence.
How to Write an Introduction, With Examples | Grammarly
Oct 20, 2022 · An introduction should include three things: a hook to interest the reader, some background on the topic so the reader can understand it, and a thesis statement that clearly …
INTRODUCTION | English meaning - Cambridge Dictionary
INTRODUCTION definition: 1. an occasion when something is put into use or brought to a place for the first time: 2. the act…. Learn more.
INTRODUCTION Definition & Meaning | Dictionary.com
What is an introduction? The introduction is the first section of an essay. It presents, or introduces, the essay topic and includes a thesis statement. Students are usually taught to write an essay …
What Is an Introduction? Definition & 25+ Examples - Enlightio
Nov 5, 2023 · An introduction is the initial section of a piece of writing, speech, or presentation wherein the author presents the topic and purpose of the material. It serves as a gateway for …
INTRODUCTION Definition & Meaning - Merriam-Webster
The meaning of INTRODUCTION is something that introduces. How to use introduction in a sentence.
How to Write an Introduction, With Examples | Grammarly
Oct 20, 2022 · An introduction should include three things: a hook to interest the reader, some background on the topic so the reader can understand it, and a thesis statement that clearly …
INTRODUCTION | English meaning - Cambridge Dictionary
INTRODUCTION definition: 1. an occasion when something is put into use or brought to a place for the first time: 2. the act…. Learn more.
INTRODUCTION Definition & Meaning | Dictionary.com
What is an introduction? The introduction is the first section of an essay. It presents, or introduces, the essay topic and includes a thesis statement. Students are usually taught to write an essay …
What Is an Introduction? Definition & 25+ Examples - Enlightio
Nov 5, 2023 · An introduction is the initial section of a piece of writing, speech, or presentation wherein the author presents the topic and purpose of the material. It serves as a gateway for …
