ארמין שמילוביץ

Changes in stochastic processes often affect their description length, and reflected by their stochastic complexity measures. Monitoring the stochastic complexity of a sequence (or, equivalently, its code length) can detect process changes that may be undetectable by traditional SPC methods. The context tree is proposed here as a universal compression algorithm for measuring the stochastic complexity of a state-dependent discrete process. The advantage of the proposed method is in the reduced number of samples that are needed for reliable monitoring.