FIRST COURSE IN PROBABILITY FOR COMPUTER AND DATA

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"In this undergraduate text, the author has distilled the core of probabilistic ideas and methods for computer and data science. The book emphasizes probabilistic and computational thinking rather than theorems and proofs. It provides insights and motivates the students by telling them why probability works and how to apply it. The unique features of the book are as follows: Bayesian probability with real-life cases in law and medicine; Logistic regression and naïve Bayes; Real-world applications of probability; Interweaving Monte Carlo simulation and probability; Gentle introduction to Markov chains and Markov chain Monte Carlo simulation. This book contains many worked examples. Numerous instructive problems scattered throughout the text are given along with problem-solving strategies. Several of the problems extend previously covered material. Answers to all problems and worked-out solutions to selected problems are also provided. Henk Tijms is the author of several textbooks in the area of applied probability and stochastic optimization. In 2008, he received the prestigious INFORMS Expository Writing Award for his work. He also contributed engaging probability puzzles to The New York Times' former Numberplay column. Request Inspection Copy Sample Chapter(s) Preface Chapter 1: Combinatorics and a Few Calculus Facts Contents: Preface Combinatorics and a Few Calculus Facts: Combinatorial Analysis The Exponential and Logarithmic Functions Fundamentals of Probability: Foundation of Probability The Concept of Conditional Probability The Law of Conditional Probability Bayesian Approach to Inference The Concept of Random Variable Expected Value and Standard Deviation Independent Random Variables and the Square Root Law Generating Functions Inequalities and the Law of Large Numbers Additional Material Useful Probability Distributions: The Binomial Distribution The Hypergeometric Distribution The Poisson Distribution The Normal Probability Density Central Limit Theorem and the Normal Distribution More on Probability Densities The Poisson Process The Q-Q Plot and the Chi-Square Test The Bivariate Normal Density Real-World Applications of Probability: Fraud in a Canadian Lottery Bombs over London in World War II Winning the Lottery Twice Santa Claus and a Baby Whisperer Birthdays and 500 Oldsmobiles Cash Winfall Lottery: A Revenue Model for Stats Geeks Coupon Collecting Benford's Law What is Casino Credit Worth? Devil's Card Game: A Psychological Test Monte Carlo Simulation and Probability: Introduction Simulation Tools Probability Applications of Simulation Bootstrap Method in Data Analysis Statistical Analysis of Simulation Output A Gentle Introduction to Markov Chains: Markov Chain Model Absorbing Markov Chains The Gambler's Ruin Problem Long-Run Behavior of Markov Chains Markov Chain Monte Carlo Simulation Solutions to Selected Problems Index Readership: Undergraduate students in computer and data science, business analytics, and operations research. Data scientists working at companies."

Applied Statistics and Probability for Engineers (7版)

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書名：Applied Statistics and Probability for Engineers 7/e Asia Edition 作者：Montgomery 出版社：WILEY 出版日期：2019/00/00 ISBN：9781119718871 內容簡介 ●New and expanded interactive features available in both WileyPLUS and the Interactive eText ●Over 100 video-based examples and problem walk-throughs. ●Check Your Understanding exercises are placed at the appropriate places in the eTextbook inside the WileyPLUS course. These simple practice exercises give students just-in-time reading comprehension self-assessment, perfect for assigning as a pre-lecture exercise. ●Video Lectures: Video lectures provide explanations of key course concepts. 內容簡介 1 The Role of Statistics in Engineering 2 Probability 3 Discrete Random Variables and Probability Distributions 4 Continuous Random Variables and Probability Distributions 5 Joint Probability Distributions 6 Descriptive Statistics 7 Point Estimation of Parameters and Sampling Distributions 8 Statistical Intervals for a Single Sample 9 Tests of Hypotheses for a Single Sample 10 Statistical Inference for Two Samples 11 Simple Linear Regression and Correlation 12 Multiple Linear Regression 13 Design and Analysis of Single-Factor Experiments: The Analysis of Variance 14 Design of Experiments with Several Factors 15 Statistical Quality Control APPENDIX A Statistical Tables and Charts APPENDIX B Bibliography APPENDIX C Summary of Confidence Intervals and Hypothesis Testing Equations for One and Two Sample Applications