No statistical model is "true" or "false," "right" or "wrong"; the models just have varying performance, which can be assessed. The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling. Although the prerequisites include only basic probability calculus and statistics, a moderate level of mathematical proficiency would be beneficial.
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No statistical model is "true" or "false," "right" or "wrong"; the models just have varying performance, which can be assessed. The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling. Although the prerequisites include only basic probability calculus and statistics, a moderate level of mathematical proficiency would be beneficial.
Imprint | Springer-Verlag New York |
Country of origin | United States |
Series | Information Science and Statistics |
Release date | November 2010 |
Availability | Expected to ship within 10 - 15 working days |
First published | 2007 |
Authors | Jorma Rissanen |
Dimensions | 235 x 155 x 8mm (L x W x T) |
Format | Paperback |
Pages | 142 |
Edition | Softcover reprint of hardcover 1st ed. 2007 |
ISBN-13 | 978-1-4419-2267-0 |
Barcode | 9781441922670 |
Categories | |
LSN | 1-4419-2267-9 |