May 1, 2024
3 minute read
The Binomial Model is a financial model that is used to value options. It is a lattice-based model that assumes that the underlying asset price can only move up or down by a fixed amount in each period. The model is named after the binomial distribution, which is the probability distribution of the number of successes in a sequence of independent experiments, each of which has a constant probability of success.
What is the Binomial Model?
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Find a path to becoming a Binomial Model. Learn more at:
OpenCourser.com/topic/xriihs/binomial
Reading list
We've selected seven 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
Binomial Model.
Provides a comprehensive introduction to probability and stochastic processes with a focus on applications in engineering and science, it includes a detailed chapter on the binomial distribution and its applications in areas like quality control and reliability engineering..
Is designed for engineering students and provides a practical introduction to probability and statistics. It includes a chapter on the binomial distribution and its applications in engineering, such as quality control and reliability analysis.
Provides a comprehensive introduction to statistical modeling and computation. It includes a chapter on the binomial distribution and its applications in statistical modeling, such as logistic regression and generalized linear models.
Provides a comprehensive introduction to statistical learning. It includes a chapter on the binomial distribution and its applications in statistical learning, such as classification and regression.
Provides a comprehensive introduction to machine learning. It includes a chapter on the binomial distribution and its applications in machine learning, such as logistic regression and support vector machines.
Provides a comprehensive introduction to deep learning. It includes a chapter on the binomial distribution and its applications in deep learning, such as convolutional neural networks and recurrent neural networks.
Provides a comprehensive introduction to reinforcement learning. It includes a chapter on the binomial distribution and its applications in reinforcement learning, such as Q-learning and policy gradients.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/xriihs/binomial
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