Читать книгу Nonlinear Filters - Simon Haykin - Страница 44

The general formulation of the optimal nonlinear Bayesian filtering leads to a computationally intractable problem; hence, the Bayesian solution is a conceptual solution. Settling for computationally tractable suboptimal solutions through deploying different approximation methods has led to a wide range of classic as well as machine learning‐based filtering algorithms. Such algorithms have their own advantages, restrictions, and domains of applicability. To assess and compare such filtering algorithms, several performance metrics can be used including entropy, Fisher information, and PCRLB. Furthermore, the Fisher information matrix is used to define the natural gradient, which is helpful in machine learning.