Understanding Probability


Book Description

Understanding Probability is a unique and stimulating approach to a first course in probability. The first part of the book demystifies probability and uses many wonderful probability applications from everyday life to help the reader develop a feel for probabilities. The second part, covering a wide range of topics, teaches clearly and simply the basics of probability. This fully revised third edition has been packed with even more exercises and examples and it includes new sections on Bayesian inference, Markov chain Monte-Carlo simulation, hitting probabilities in random walks and Brownian motion, and a new chapter on continuous-time Markov chains with applications. Here you will find all the material taught in an introductory probability course. The first part of the book, with its easy-going style, can be read by anybody with a reasonable background in high school mathematics. The second part of the book requires a basic course in calculus.




A Modern Introduction to Probability and Statistics


Book Description

Suitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included – this is a modern method missing in many other books




Understanding Probability


Book Description

In this fully revised second edition of Understanding Probability, the reader can learn about the world of probability in an informal way. The author demystifies the law of large numbers, betting systems, random walks, the bootstrap, rare events, the central limit theorem, the Bayesian approach and more. This second edition has wider coverage, more explanations and examples and exercises, and a new chapter introducing Markov chains, making it a great choice for a first probability course. But its easy-going style makes it just as valuable if you want to learn about the subject on your own, and high school algebra is really all the mathematical background you need.




Probability and Statistics


Book Description

Unlike traditional introductory math/stat textbooks, Probability and Statistics: The Science of Uncertainty brings a modern flavor based on incorporating the computer to the course and an integrated approach to inference. From the start the book integrates simulations into its theoretical coverage, and emphasizes the use of computer-powered computation throughout.* Math and science majors with just one year of calculus can use this text and experience a refreshing blend of applications and theory that goes beyond merely mastering the technicalities. They'll get a thorough grounding in probability theory, and go beyond that to the theory of statistical inference and its applications. An integrated approach to inference is presented that includes the frequency approach as well as Bayesian methodology. Bayesian inference is developed as a logical extension of likelihood methods. A separate chapter is devoted to the important topic of model checking and this is applied in the context of the standard applied statistical techniques. Examples of data analyses using real-world data are presented throughout the text. A final chapter introduces a number of the most important stochastic process models using elementary methods. *Note: An appendix in the book contains Minitab code for more involved computations. The code can be used by students as templates for their own calculations. If a software package like Minitab is used with the course then no programming is required by the students.




Probability, Statistics, and Truth


Book Description

This comprehensive study of probability considers the approaches of Pascal, Laplace, Poisson, and others. It also discusses Laws of Large Numbers, the theory of errors, and other relevant topics.




Introduction to Probability


Book Description

Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for 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 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.




Understanding Probability Models


Book Description

"This book intends to highlight how the Theory of Probability supports, not only statistical modeling but how it allows describing different real life phenomena. It gives clues for understanding the philosophic roots of probability and how they are present in different areas of knowledge. The readers may use the book as a source for understanding the philosophical development of probability concepts and of the intents to obtain mathematical models. The chapters deal with the understanding of how probability models are usable for determining: A Probabilistic model of the best flight value for the design on paper of a helicopter How to model the improvement of the behavior of water heating systems and of the reliability of systems Models for determining the probability of non responses in inquiries and to evaluate the missing data. The modeling of various problems related with the behavior of ordering models of use in decision rules and of general properties of Order Statistics. A unified study of the probabilistic aspects of two Metaheuristics: Simulated Annealing and Tabu Search. How to obtain the identification of econometric techniques for dealing efficiently with the study of economic growth models under endogeneity. This book will be of interest for biometricians, statisticians, economists, engineers dealing with control and reliability, as well for informaticians"--




Chances Are--


Book Description

Do you avoid teaching probability and statistics because the subjects seem confusing and complex? Are you less than sure about your knowledge of the topics? Let Chances Are . . . take you and your students on a fun and exciting mathematical journey none of you will ever forget! Filled with easy-to-understand explanations and creative activities, this book offers teachers a simple method for teaching probability and statistics in an enjoyable way. This book can serve as an introduction for any beginner, from gifted and advanced students in upper elementary school, to high school students needing enrichment or preparation for Advanced Placement Statistics or future college courses. From helping to win a card game, to making life-or-death medical decisions, the uses of probability and statistics are virtually endless. For teachers of elementary students, the book offers simple, hands-on lessons and activities about probability and basic statistics. For teachers of older students, advanced statistical concepts are discussed and activities are provided. Reviewers have found the book's level to be appropriate for a wide range of ages, from fourth graders to post-secondary students.




Introduction to Probability, Statistics, and Random Processes


Book Description

The book covers basic concepts such as random experiments, probability axioms, conditional probability, and counting methods, single and multiple random variables (discrete, continuous, and mixed), as well as moment-generating functions, characteristic functions, random vectors, and inequalities; limit theorems and convergence; introduction to Bayesian and classical statistics; random processes including processing of random signals, Poisson processes, discrete-time and continuous-time Markov chains, and Brownian motion; simulation using MATLAB and R.