Recursive nonlinear estimation : a geometric approach
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 an appealing Pythagorean-like geometry of the empirical and model distributions, the book brings a new solution t...
Guardat en:
| Autor principal: | |
|---|---|
| Format: | Livre numérique |
| Idioma: | Anglais |
| Publicat: |
Berlin [etc.] :
Springer-Verlag London Limited : Springer e-books
[20..].
Cham : Springer Nature |
| Col·lecció: | Lecture notes in control and information sciences
216 |
| Matèries: | |
| Accés en línia: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Recursive nonlinear estimation, a geometric approach, Rudolf Kulhavý, Berlin, Springer, 1996, 1 vol (xvi, 224 p.), Lecture notes in control and information sciences, 3-540-76063-6 • Recursive Nonlinear Estimation, Texte imprimé, 9783662173404 |
Taula de continguts:
- Inference under constraints
- From matching data to matching probabilities
- Optimal estimation with compressed data
- Approximate estimation with compressed data
- Numerical implementation
- Concluding remarks
- Selected topics from probability theory
- Selected topics from convex optimization.