Recursive Nonlinear Estimation

In a close analogy to matching data in Euclidean space, this monograph views parameter estimation as matching of the empirical distribution of data with a model-based distribution. Using a Pythagorean-like geometry of the empirical and model distributions, the book suggests a solution to the problem of recursive estimation of non-Gaussian and nonlinear models which can be regarded as a specific approximation of Bayesian estimation. The cases of independent observations and controlled dynamic systems are considered in parallel; orm er case gives insight into the latter case, which should be of interest to the control community. A number of examples illustrate the key concepts and tools used.