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Information and complexity in statistical modeling

By: Material type: TextTextSeries: Information science and statisticsPublication details: 2007 Springer Science+Business Media New YorkDescription: xii., 142 p., ill., 17 cmISBN:
  • 9780387366104
Subject(s): DDC classification:
  • 519.5 R4I6
Summary: 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. (http://www.springer.com/computer/theoretical+computer+science/book/978-0-387-36610-4)
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Item type Current library Item location Shelving location Call number Status Date due Barcode
Books Vikram Sarabhai Library Rack 28-B / Slot 1415 (0 Floor, East Wing) General Stacks 519.5 R4I6 (Browse shelf(Opens below)) Available 173699

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. (http://www.springer.com/computer/theoretical+computer+science/book/978-0-387-36610-4)

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