Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 55-61 |
| Journal | Communications in dependability and quality management : an international journal |
| Volume | 8 |
| Issue number | 3 |
| State | Published - 2005 |
Keywords
- Process control
- Control charts
- Stochastic complexity
- Context tree algorithm