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
Autumn 2022
The course addresses both classic and recent developments in counting and sampling. It covers counting complexity, exact counting via determinants, sampling via Markov chains, and high-dimensional expanders.
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