Information and complexity in statistical modeling
Material type:
- 9780387366104
- 519.5 R4I6
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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