Master stochastic processes with clarity, rigor, and real-world insight.
Stochastic Processes & Applied Probability: A First Course in Modeling Random Systems – Volume 1 is a carefully structured introduction designed for upper-level undergraduate and early graduate students in mathematics, statistics, engineering, operations research, economics, and data science.
Unlike many traditional texts that are overly abstract or theorem-heavy, this book emphasizes understanding through worked examples, modeling intuition, and step-by-step problem solving.
This volume develops the mathematical foundation behind systems that evolve under uncertainty — from random walks and queueing systems to Markov chains and Brownian motion.
Inside this book you will learn:
• Probability refresher and conditioningThis textbook includes:
Fully worked examples with clear step-by-step solutions
Progressive difficulty from fundamentals to applications
Diagnostic reviews and mastery checkpoints
Common-trap sections to prevent frequent mistakes
Retention reviews and cumulative practice
Complete problem solutions and answer summaries
Modeling-focused explanations that connect theory with applications
Whether you are studying stochastic processes for mathematics, engineering, data science, operations research, finance, or self-study, this book provides a practical and rigorous path into one of the most powerful areas of applied mathematics.
Learn the theory. Understand the models. Apply stochastic thinking with confidence.
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Paperback. Condition: new. Paperback. Master stochastic processes with clarity, rigor, and real-world insight.Stochastic Processes & Applied Probability: A First Course in Modeling Random Systems - Volume 1 is a carefully structured introduction designed for upper-level undergraduate and early graduate students in mathematics, statistics, engineering, operations research, economics, and data science.Unlike many traditional texts that are overly abstract or theorem-heavy, this book emphasizes understanding through worked examples, modeling intuition, and step-by-step problem solving.This volume develops the mathematical foundation behind systems that evolve under uncertainty - from random walks and queueing systems to Markov chains and Brownian motion.Inside this book you will learn: - Probability refresher and conditioning- Law of total probability and Bayes' theorem- Conditional expectation and modeling intuition- Random walks and gambler's ruin- Generating functions and branching processes- Discrete-time Markov chains- State classification and long-run behavior- Absorbing chains and first-passage analysis- The Poisson process- Continuous-time Markov chains- Queueing theory and M/M systems- Brownian motion and introductory diffusion modelsThis textbook includes: Fully worked examples with clear step-by-step solutions Progressive difficulty from fundamentals to applications Diagnostic reviews and mastery checkpoints Common-trap sections to prevent frequent mistakes Retention reviews and cumulative practice Complete problem solutions and answer summaries Modeling-focused explanations that connect theory with applicationsWhether you are studying stochastic processes for mathematics, engineering, data science, operations research, finance, or self-study, this book provides a practical and rigorous path into one of the most powerful areas of applied mathematics.Learn the theory. Understand the models. Apply stochastic thinking with confidence. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798199354479
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