In this chapter the identification problems are approached via Bayesian statistics. In Bayesian view the concept of probability is not interpreted in terms of limits of relative frequencies but more generally as a subjective measure of belief of a rationally and consistently reasoning person (here called the statistician) which is used to describe quantitatively the uncertain relationship between the statistician and the external world ...
Content:
- Underlying Philosophy and Basic Relations
- System Model, Reexamined from Bayesian Viewpoint
- Parameter Estimation and Output Prediction
- Time-Varying Parameters and Adaptivity
- System Classification
The book provides the theoretical and algorithmic basis of optimized probabilistic advising. Starting from abstract ideas and formulations, and culminating in detailed algorithms, the book comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modeling by dynamic mixture models and fully probabilistic dynamic optimization.
