FIRST COURSE IN PROBABILITY FOR COMPUTER AND DATA SCIENCE, AIn 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: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.Contents: PrefaceCombinatorics and a Few Calculus Facts:Combinatorial AnalysisThe Exponential and Logarithmic FunctionsFundamentals of Probability:Foundation of ProbabilityThe Concept of Conditional ProbabilityThe Law of Conditional ProbabilityBayesian Approach to InferenceThe Concept of Random VariableExpected Value and Standard DeviationIndependent Random Variables and the Square Root LawGenerating FunctionsInequalities and the Law of Large NumbersAdditional MaterialUseful Probability Distributions:The Binomial DistributionThe Hypergeometric DistributionThe Poisson DistributionThe Normal Probability DensityCentral Limit Theorem and the Normal DistributionMore on Probability DensitiesThe Poisson ProcessThe Q-Q Plot and the Chi-Square TestThe Bivariate Normal DensityReal-World Applications of Probability:Fraud in a Canadian LotteryBombs over London in World War IIWinning the Lottery TwiceSanta Claus and a Baby WhispererBirthdays and 500 OldsmobilesCash Winfall Lottery: A Revenue Model for Stats GeeksCoupon CollectingBenford’s LawWhat is Casino Credit Worth’Devil’s Card Game: A Psychological TestMonte Carlo Simulation and Probability:IntroductionSimulation ToolsProbability Applications of SimulationBootstrap Method in Data AnalysisStatistical Analysis of Simulation OutputA Gentle Introduction to Markov Chains:Markov Chain ModelAbsorbing Markov ChainsThe Gambler’s Ruin ProblemLong-Run Behavior of Markov ChainsMarkov Chain Monte Carlo SimulationSolutions to Selected ProblemsIndexReadership: Undergraduate students in computer and data science, business analytics, and operations research. Data scientists working at companies.Key Features: Emphasis on probabilistic and computational thinking Interweaving Monte Carlo simulation and probability Real-world applications of probabilityISBN: 9789811271748, 9811271747
