Description

Book Synopsis

This text introduces engineering students to probability theory and stochastic processes. Along with thorough mathematical development of the subject, the book presents intuitive explanations of key points in order to give students the insights they need to apply math to practical engineering problems. The first five chapters contain the core material that is essential to any introductory course. In one-semester undergraduate courses, instructors can select material from the remaining chapters to meet their individual goals. Graduate courses can cover all chapters in one semester.



Table of Contents

Chapter 1. Experiments, Models, and Probabilities

Chapter 2. Sequential Experiments

Chapter 3. Discrete Random Variables

Chapter 4. Continuous Random Variables

Chapter 5. Multiple Random Vectors

Chapter 6. Probability Models of Derived Random Variables

Chapter 7. Conditional Probability Models

Chapter 8. Random Vectors

Chapter 9. Sums of Random Variables

Chapter 10. The Sample Mean

Chapter 11. Hypothesis Testing

Chapter 12. Estimation of a Random Variable

Chapter 13. Stochastic Processes

Probability and Stochastic Processes

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    A Paperback / softback by Roy D. Yates, David J. Goodman

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      View other formats and editions of Probability and Stochastic Processes by Roy D. Yates

      Publisher: John Wiley & Sons Inc
      Publication Date: Publication Date: 21/10/2014
      ISBN13: 9781118808719, 978-1118808719
      ISBN10: 1118808711

      Description

      Book Synopsis

      This text introduces engineering students to probability theory and stochastic processes. Along with thorough mathematical development of the subject, the book presents intuitive explanations of key points in order to give students the insights they need to apply math to practical engineering problems. The first five chapters contain the core material that is essential to any introductory course. In one-semester undergraduate courses, instructors can select material from the remaining chapters to meet their individual goals. Graduate courses can cover all chapters in one semester.



      Table of Contents

      Chapter 1. Experiments, Models, and Probabilities

      Chapter 2. Sequential Experiments

      Chapter 3. Discrete Random Variables

      Chapter 4. Continuous Random Variables

      Chapter 5. Multiple Random Vectors

      Chapter 6. Probability Models of Derived Random Variables

      Chapter 7. Conditional Probability Models

      Chapter 8. Random Vectors

      Chapter 9. Sums of Random Variables

      Chapter 10. The Sample Mean

      Chapter 11. Hypothesis Testing

      Chapter 12. Estimation of a Random Variable

      Chapter 13. Stochastic Processes

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