{"product_id":"information-and-complexity-in-statistical-modeling-hardcover","title":"Information and Complexity in Statistical Modeling - Hardcover","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eJorma Rissanen\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis volume presents a different, yet logically unassailable, view of statistical modeling. It details a method of 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. The view of the modeling problem presented in this book permits a unified treatment of any type of parameters, their number, and even their structure. Since only optimally distinguished models are worthy of testing, a logically sound and straightforward treatment of hypothesis testing is found in which the confidence in the test result can be assessed. The techniques presented in this book have application in all fields of modern engineering, including signal and image processing, bioinformatics, pattern recognition, and machine learning.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eNo 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. Inspired by Kolmogorov's structure function in the algorithmic theory of complexity, this is accomplished by finding the shortest code length, called the stochastic complexity, with which the data can be encoded when advantage is taken of the models in a suggested class, which amounts to the MDL (Minimum Description Length) principle. The complexity, in turn, breaks up into the shortest code length for the optimal model in a set of models that can be optimally distinguished from the given data and the rest, which defines \"noise\" as the incompressible part in the data without useful information.\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003eSuch a view of the modeling problem permits a unified treatment of any type of parameters, their number, and even their structure. Since only optimally distinguished models are worthy of testing, we get a logically sound and straightforward treatment of hypothesis testing, in which for the first time the confidence in the test result can be assessed. Although the prerequisites include only basic probability calculus and statistics, a moderate level of mathematical proficiency would be beneficial. The different and logically unassailable view of statistical modelling should provide excellent grounds for further research and suggest topics for graduate students in all fields of modern engineering, including and not restricted to signal and image processing, bioinformatics, pattern recognition, and machine learning to mention just a few.\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003eThe author is an Honorary Doctor and Professor Emeritus of the Technical University of Tampere, Finland, a Fellow of Helsinki Institute for Information Technology, and visiting Professor in the\u003c\/p\u003e \u003cp\u003eComputer Learning Research Center of University of London, Holloway, England. He is also a Foreign Member of Finland's Academy of Science and Letters, an Associate Editor of \u003cem\u003eIMA Journal of Mathematical Control and Information\u003c\/em\u003e and of \u003cem\u003eEURASIP Journal on Bioinformatics and Systems Biology.\u003c\/em\u003e He is also a former Associate Editor of \u003cem\u003eSource Coding of IEEE Transactions on Information Theory.\u003c\/em\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003eThe author is the recipient of the IEEE Information Theory Society's 1993 Richard W. Hamming medal for fundamental contributions to information theory, statistical inference, control theory, and the theory of complexity; the Information Theory Society's Golden Jubilee Award in 1998 for Technological Innovation for inventing Arithmetic Coding; and the 2006 Kolmogorov medal by University of London. He has also received an IBM Corporate Award for the \u003cem\u003eMDL\u003c\/em\u003e and \u003cem\u003ePMDL \u003c\/em\u003ePrinciples in 1991, and two best paper awards.\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 142\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.56 x 9.27 x 6.51 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e January 25, 2007\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47214171062521,"sku":"9780387366104","price":89.08,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/989AkURqfN9780387366104.webp?v=1768112283","url":"https:\/\/bookscloud.io\/products\/information-and-complexity-in-statistical-modeling-hardcover","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}