Quantization for probability distributions refers broadly to estimating a given probability measure by a discrete probability measure supported by a finite number of points. We consider general geometric approaches to quantization using stationary processes arising in dynamical systems, followed by a discussion of the special cases of stationary processes: random processes and Diophantine processes. We are interested in how close stationary process can be to giving optimal n-means and nth optimal mean distortion errors. We also consider different ways of measuring the degree of approximation by quantization, and their advantages and disadvantages in these different contexts.
Joseph Rosenblatt & Mrinal Kanti Roychowdhury (2020) Optimal quantization via dynamics, Dynamical Systems, 35:3, 522-538, DOI: 10.1080/14689367.2020.1727416