• Title: Applied Probability
• Author(s) Paul Pfeiffer
• Publisher: OpenStax-CNX (2020); eBook (Creative Commons Licensed)
• License(s): Creative Commons License (CC)
• Hardcover/Paperback: N/A
• eBook: HTML, PDF (634 pages), ePub, Kindle, etc.
• Language: English
• ISBN-10/ASIN: N/A
• ISBN-13: N/A
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Book Description

This book presents a unique blend of theory and applications, with special emphasis on mathematical modeling, computational techniques, and examples from the real world application in industries and science.

This is a "first course" in the sense that it presumes no previous course in probability. The mathematical prerequisites are ordinary calculus and the elements of matrix algebra. A few standard series and integrals are used, and double integrals are evaluated as iterated integrals. The reader who can evaluate simple integrals can learn quickly from the examples how to deal with the iterated integrals used in the theory of expectation and conditional expectation. Appendix B provides a convenient compendium of mathematical facts used frequently in this work.

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• Probability Theory and Stochastic Process
• Data Science and Engineering
• Python Programming
• MATLAB Programming

• Applied Probability (Paul Pfeiffer)
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