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Serving as the foundation for a one-semester course in stochastic processes for students familiar with elementary probability theory and calculus, Introduction to Stochastic Modeling, Third Edition, bridges the gap between basic probability and an intermediate level course in stochastic processes.The objectives of the text are to introduce students to the standard concepts and methods of . A stochastic model predicts a set of possible outcomes weighed by their likelihoods or probabilities. This book is intended for professionals in data science, computer science, operations research, statistics, machine learning, big data, and mathematics. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models . Suitable for advanced undergraduates and graduate-level industrial engineers and . Examples abound, from the simple equation S = Zgt2 . Savage, et al. 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