Stochastic Processes & Applied Probability

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…