The optional stopping theorem states that, under certain conditions, the expected value of a martingale at a stopping time is equal to its initial expected value. This means that, on average, nothing can be gained by stopping play based on the information obtainable so far. It is an important tool in mathematical finance and applies to doubling strategies.
Stanford 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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