Stationary processes are used in time series analysis to model data that exhibit no trend or seasonality. Parameters such as mean and variance remain constant over time, and the process can be used to predict future values. Non-stationary data can be transformed to become stationary by removing trends or seasonal cycles.
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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