A stationary distribution is a specific entity which is unchanged by the effect of some matrix or operator. It is related to eigenvectors for which the eigenvalue is unity and can be referred to as a stable distribution in some fields of application. It may not be unique.

Stanford University

Spring 2022

CS 168 provides a comprehensive introduction to modern algorithm concepts, covering hashing, dimension reduction, programming, gradient descent, and regression. It emphasizes both theoretical understanding and practical application, with each topic complemented by a mini-project. It's suitable for those who have taken CS107 and CS161.

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+ 57 more conceptsStanford University

Fall 2022

This course dives into the use of randomness in algorithms and data structures, emphasizing the theoretical foundations of probabilistic analysis. Topics range from tail bounds, Markov chains, to randomized algorithms. The concepts are applied to machine learning, networking, and systems. Prerequisites indicate intermediate-level understanding required.

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