01201nam a2200157Ia 4500008004100000020001800041082001600059100002000075245005500095260005200150300003100202365001500233440003900248520074100287650001501028140323b2007 xxu||||| |||| 00| 0 eng d a9780387366104 a519.5bR4I6 aRissanen, Jorma aInformation and complexity in statistical modeling c2007bSpringer Science+Business MediaaNew York axii., 142 p., ill., 17 cm. aEURb40.95 aInformation science and statistics aNo 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) aStatistics