Process parameter and state estimation

The paper presents a coherent picture of the parameter-estimation problem. Starting from the theory of minimum risk- or Bayes estimation the paper shows how other statistical estimation techniques can be interpreted as special cases (viz. maximum likelihood-, Markov-, and least squares estimation), The most important properties of these estimates are summarized. The engineering approaches based on these statistical techniques can be divided into two classes, viz. ''using explicit mathematical relations'' and ''using adjustment of a model''. Each of these classes is discussed briefly. The majority of parameter estimation techniques can be embodied in this framework. A very brief discussion is given on the problem of process state estimation which is related to parameter estimation. A few examples are used to illustrate the notions presented and to indicate some engineering considerations.