Random Sets: Theory and Applications: The IMA Volumes in Mathematics and its Applications, cartea 97
Editat de John Goutsias, Ronald P.S. Mahler, Hung T. Nguyenen Limba Engleză Paperback – 6 oct 2012
Toate formatele și edițiile | Preț | Express |
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Paperback (1) | 647.27 lei 6-8 săpt. | |
Springer – 6 oct 2012 | 647.27 lei 6-8 săpt. | |
Hardback (1) | 653.65 lei 6-8 săpt. | |
Springer – 30 oct 1997 | 653.65 lei 6-8 săpt. |
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Specificații
ISBN-13: 9781461273509
ISBN-10: 1461273501
Pagini: 436
Ilustrații: XIV, 416 p.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.61 kg
Ediția:Softcover reprint of the original 1st ed. 1997
Editura: Springer
Colecția Springer
Seria The IMA Volumes in Mathematics and its Applications
Locul publicării:New York, NY, United States
ISBN-10: 1461273501
Pagini: 436
Ilustrații: XIV, 416 p.
Dimensiuni: 155 x 235 x 23 mm
Greutate: 0.61 kg
Ediția:Softcover reprint of the original 1st ed. 1997
Editura: Springer
Colecția Springer
Seria The IMA Volumes in Mathematics and its Applications
Locul publicării:New York, NY, United States
Public țintă
ResearchCuprins
I. Image Modeling and Analysis.- Morphological analysis of random sets. An introduction.- Statistical problems for random sets.- On estimating granulometric discrete size distributions of random sets.- Logical granulometric filtering in the signal-union-clutter model.- On optimal filtering of morphologically smooth discrete random sets and related open problems.- II. Information/Data Fusion and Expert Systems.- On the maximum of conditional entropy for upper/lower probabilities generated by random sets.- Random sets in information fusion. An overview.- Cramér-Rao type bounds for random set problems.- Random sets in data fusion. Multi-object state-estimation as a foundation of data fusion theory.- Extension of relational and conditional event algebra to random sets with applications to data fusion.- Belief functions and random sets.- III. Theoretical Statistics and Expert Systems.- Uncertainty measures, realizations and entropies.- Random sets in decision-making.- Random sets unify, explain, and aid known uncertainty methods in expert systems.- Laws of large numbers for random sets.- Geometric structure of lower probabilities.- Some static and dynamic aspects of robust Bayesian theory.- List of Participants.