Information and Complexity in Statistical Modeling - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Information and Complexity in Statistical Modeling write by Jorma Rissanen. This book was released on 2007-12-15. Information and Complexity in Statistical Modeling available in PDF, EPUB and Kindle. 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.
Stochastic Complexity In Statistical Inquiry
Stochastic Complexity In Statistical Inquiry - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Stochastic Complexity In Statistical Inquiry write by Jorma Rissanen. This book was released on 1998-10-07. Stochastic Complexity In Statistical Inquiry available in PDF, EPUB and Kindle. This book describes how model selection and statistical inference can be founded on the shortest code length for the observed data, called the stochastic complexity. This generalization of the algorithmic complexity not only offers an objective view of statistics, where no prejudiced assumptions of 'true' data generating distributions are needed, but it also in one stroke leads to calculable expressions in a range of situations of practical interest and links very closely with mainstream statistical theory. The search for the smallest stochastic complexity extends the classical maximum likelihood technique to a new global one, in which models can be compared regardless of their numbers of parameters. The result is a natural and far reaching extension of the traditional theory of estimation, where the Fisher information is replaced by the stochastic complexity and the Cramer-Rao inequality by an extension of the Shannon-Kullback inequality. Ideas are illustrated with applications from parametric and non-parametric regression, density and spectrum estimation, time series, hypothesis testing, contingency tables, and data compression.
Statistical Modeling and Analysis for Complex Data Problems
Statistical Modeling and Analysis for Complex Data Problems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Statistical Modeling and Analysis for Complex Data Problems write by Pierre Duchesne. This book was released on 2005-04-12. Statistical Modeling and Analysis for Complex Data Problems available in PDF, EPUB and Kindle. STATISTICAL MODELING AND ANALYSIS FOR COMPLEX DATA PROBLEMS treats some of today’s more complex problems and it reflects some of the important research directions in the field. Twenty-nine authors—largely from Montreal’s GERAD Multi-University Research Center and who work in areas of theoretical statistics, applied statistics, probability theory, and stochastic processes—present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains. Some of the areas and topics examined in the volume are: an analysis of complex survey data, the 2000 American presidential election in Florida, data mining, estimation of uncertainty for machine learning algorithms, interacting stochastic processes, dependent data & copulas, Bayesian analysis of hazard rates, re-sampling methods in a periodic replacement problem, statistical testing in genetics and for dependent data, statistical analysis of time series analysis, theoretical and applied stochastic processes, and an efficient non linear filtering algorithm for the position detection of multiple targets. The book examines the methods and problems from a modeling perspective and surveys the state of current research on each topic and provides direction for further research exploration of the area.
Statistical Complexity
Statistical Complexity - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Statistical Complexity write by K.D. Sen. This book was released on 2011-08-27. Statistical Complexity available in PDF, EPUB and Kindle. The understanding of electron density as the carrier of all the information of a multielectronic system is implicit in the theorems of density functional theory. Information theoretical based measures giving a quantitative understanding of statistical complexity of such systems is shaping up as a new area of research in chemical physics. This book is the first monograph of its kind covering the aspects of complexity measure in atoms and molecules.
Models of Science Dynamics
Models of Science Dynamics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Models of Science Dynamics write by Andrea Scharnhorst. This book was released on 2012-01-24. Models of Science Dynamics available in PDF, EPUB and Kindle. Models of Science Dynamics aims to capture the structure and evolution of science, the emerging arena in which scholars, science and the communication of science become themselves the basic objects of research. In order to capture the essence of phenomena as diverse as the structure of co-authorship networks or the evolution of citation diffusion patterns, such models can be represented by conceptual models based on historical and ethnographic observations, mathematical descriptions of measurable phenomena, or computational algorithms. Despite its evident importance, the mathematical modeling of science still lacks a unifying framework and a comprehensive study of the topic. This volume fills this gap, reviewing and describing major threads in the mathematical modeling of science dynamics for a wider academic and professional audience. The model classes presented cover stochastic and statistical models, system-dynamics approaches, agent-based simulations, population-dynamics models, and complex-network models. The book comprises an introduction and a foundational chapter that defines and operationalizes terminology used in the study of science, as well as a review chapter that discusses the history of mathematical approaches to modeling science from an algorithmic-historiography perspective. It concludes with a survey of remaining challenges for future science models and their relevance for science and science policy.
