Applied Probability and Stochastic Processes
by Frank Beichelt
This text presents a self-contained introduction to elementary probability theory and stochastic processes with a special emphasis on their applications in science, engineering, finance, computer science, and operations research. This edition provides a completely rewritten and expanded part on probability theory. Along with additional examples,
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Applied Probability and Stochastic Processes, Second Edition presents a self-contained introduction to elementary probability theory and stochastic processes with a special emphasis on their applications in science, engineering, finance, computer science, and operations research. It covers the theoretical foundations for modeling time-dependent random phenomena in these areas and illustrates applications through the analysis of numerous practical examples. The author draws on his 50 years of experience in the field to give your students a better understanding of probability theory and stochastic processes and enable them to use stochastic modeling in their to the Second Edition
- Completely rewritten part on probability theory—now more than double in size
- New sections on time series analysis, random walks, branching processes, and spectral analysis of stationary stochastic processes
- Comprehensive numerical discussions of examples, which replace the more theoretically challenging sections
- Additional examples, exercises, and figures
Table of Contents
PROBABILITY THEORY: RANDOM EVENTS AND THEIR PROBABILITIES. ONE-DIMENSIONAL RANDOM VARIABLES. MULTIDIMENSIONAL RANDOM VARIABLES. FUNCTIONS OF RANDOM VARIABLES. INEQUALITIES AND LIMIT THEOREMS. STOCHASTIC PROCESSES: BASICS OF STOCHASTIC PROCESSES. RANDOM POINT PROCESSES. DISCRETE-TIME MARKOV CHAINS. CONTINUOUS-TIME MARKOV CHAINS. MARTINGALES. BROWNIAN MOTION. SPECTRAL ANALYSIS OF STATIONARY PROCESSES. REFERENCES. INDEX.
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