May 1, 2024
Updated May 11, 2025
20 minute read
Expected value is a fundamental concept in probability theory and statistics that represents the long-term average outcome of a random event if it were repeated many times. It's a powerful tool for decision-making under uncertainty, allowing individuals and organizations to quantify potential outcomes and make more informed choices. Understanding expected value can be particularly engaging when you realize its broad applicability, from everyday decisions like whether to buy a lottery ticket or purchase insurance, to complex strategic choices in finance, business, and even fields like machine learning and engineering. The ability to weigh probabilities and potential payoffs (or losses) provides a structured way to approach situations where the future is unknown.
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Reading list
We've selected six books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Expected Value.
Focuses on the application of expected value in finance, particularly in the areas of asset pricing, portfolio optimization, and risk management. It is written by John C. Hull, a leading expert in financial mathematics and a professor at the University of Toronto.
Provides a comprehensive overview of expected value, covering both theoretical foundations and practical applications in various fields. It is written by Thomas S. Ferguson, a renowned statistician known for his contributions to probability theory.
Presents a Bayesian approach to expected value and statistical inference. It covers topics such as probability distributions, Bayesian estimation, and hypothesis testing. It is written by David A. Freedman, a renowned statistician and a professor at the University of California, Berkeley.
Explores the use of expected value in stochastic processes, covering topics such as Markov chains, Brownian motion, and stochastic differential equations. It is written by Ioannis Karatzas and Steven E. Shreve, both of whom are leading researchers in the field of stochastic processes.
Examines the use of expected value in non-parametric statistics, covering topics such as order statistics, rank tests, and non-parametric regression. It is written by Pranab K. Sen, a leading researcher in non-parametric statistics and a professor at the University of North Carolina at Chapel Hill.
Examines the application of expected value in computer science, covering topics such as algorithms, data structures, and complexity theory. It is written by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, all of whom are leading researchers in computer science and professors at the Massachusetts Institute of Technology.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/3vorai/expected
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