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Item Details
| Title:
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PATTERN THEORY
THE STOCHASTIC ANALYSIS OF REAL-WORLD SIGNALS |
| By: |
David Mumford, Agnes Desolneux |
| Format: |
Hardback |
| List price:
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£89.99 |
| Our price: |
£80.99 |
| Discount: |
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| You save:
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£9.00 |
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| ISBN 10: |
1568815794 |
| ISBN 13: |
9781568815794 |
| Availability: |
Usually dispatched within 3-5 days.
 Delivery
rates
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| Stock: |
Currently 1item in stock |
| Publisher: |
TAYLOR & FRANCIS INC |
| Pub. date: |
2 April, 2010 |
| Pages: |
375 |
| Description: |
Pattern theory is a distinctive approach to the analysis of all forms of real-world signals. At its core is the design of a large variety of probabilistic models whose samples reproduce the look and feel of the real signals, their patterns, and their variability. Bayesian statistical inference then allows you to apply these models in the analysis of new signals. This book treats the mathematical tools, the models themselves, and the computational algorithms for applying statistics to analyze six representative classes of signals of increasing complexity. The book covers patterns in text, sound, and images. Discussions of images include recognizing characters, textures, nature scenes, and human faces. The text includes online access to the materials (data, code, etc.) needed for the exercises. |
| Synopsis: |
Pattern theory is a distinctive approach to the analysis of all forms of real-world signals. At its core is the design of a large variety of probabilistic models whose samples reproduce the look and feel of the real signals, their patterns, and their variability. Bayesian statistical inference then allows you to apply these models in the analysis of new signals. This book treats the mathematical tools, the models themselves, and the computational algorithms for applying statistics to analyze six representative classes of signals of increasing complexity. The book covers patterns in text, sound, and images. Discussions of images include recognizing characters, textures, nature scenes, and human faces. The text includes online access to the materials (data, code, etc.) needed for the exercises. |
| Publication: |
US |
| Imprint: |
A K Peters |
| Returns: |
Returnable |
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