Автор: Trevor Hastie; Robert Tibshirani; Jerome Friedman Название: The Elements of Statistical Learning ISBN: 0387848576 ISBN-13(EAN): 9780387848570 Издательство: Springer Рейтинг: Цена: 69870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This major new edition features many topics not covered in the original, including graphical models, random forests, and ensemble methods. As before, it covers the conceptual framework for statistical data in our rapidly expanding computerized world.
Автор: Malley Название: Statistical Learning for Biomedical Data ISBN: 0521699096 ISBN-13(EAN): 9780521699099 Издательство: Cambridge Academ Рейтинг: Цена: 43290.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Biomedical researchers need machine learning techniques to make predictions such as survival/death or response to treatment when data sets are large and complex. This highly motivating introduction to these machines explains underlying principles in nontechnical language, using many examples and figures, and connects these new methods to familiar techniques.
Автор: Del Moral Название: Mean Field Simulation for Monte Carlo Integration ISBN: 1138198730 ISBN-13(EAN): 9781138198739 Издательство: Taylor&Francis Рейтинг: Цена: 53070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
In the last three decades, there has been a dramatic increase in the use of interacting particle methods as a powerful tool in real-world applications of Monte Carlo simulation in computational physics, population biology, computer sciences, and statistical machine learning. Ideally suited to parallel and distributed computation, these advanced particle algorithms include nonlinear interacting jump diffusions; quantum, diffusion, and resampled Monte Carlo methods; Feynman-Kac particle models; genetic and evolutionary algorithms; sequential Monte Carlo methods; adaptive and interacting Markov chain Monte Carlo models; bootstrapping methods; ensemble Kalman filters; and interacting particle filters.
Mean Field Simulation for Monte Carlo Integration presents the first comprehensive and modern mathematical treatment of mean field particle simulation models and interdisciplinary research topics, including interacting jumps and McKean-Vlasov processes, sequential Monte Carlo methodologies, genetic particle algorithms, genealogical tree-based algorithms, and quantum and diffusion Monte Carlo methods.
Along with covering refined convergence analysis on nonlinear Markov chain models, the author discusses applications related to parameter estimation in hidden Markov chain models, stochastic optimization, nonlinear filtering and multiple target tracking, stochastic optimization, calibration and uncertainty propagations in numerical codes, rare event simulation, financial mathematics, and free energy and quasi-invariant measures arising in computational physics and population biology.
This book shows how mean field particle simulation has revolutionized the field of Monte Carlo integration and stochastic algorithms. It will help theoretical probability researchers, applied statisticians, biologists, statistical physicists, and computer scientists work better across their own disciplinary boundaries.
Автор: V.B. Melas; Stefania Mignani; Paola Monari; Luigi Название: Topics in Statistical Simulation ISBN: 1493921037 ISBN-13(EAN): 9781493921034 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The Department of Statistical Sciences of the University of Bologna in collaboration with the Department of Management and Engineering of the University of Padova, the Department of Statistical Modelling of Saint Petersburg State University, and INFORMS Simulation Society sponsored the Seventh Workshop on Simulation.
Автор: V.B. Melas; Stefania Mignani; Paola Monari; Luigi Название: Topics in Statistical Simulation ISBN: 1493947842 ISBN-13(EAN): 9781493947843 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The Department of Statistical Sciences of the University of Bologna in collaboration with the Department of Management and Engineering of the University of Padova, the Department of Statistical Modelling of Saint Petersburg State University, and INFORMS Simulation Society sponsored the Seventh Workshop on Simulation.
Автор: Chjan Lim; Joseph Nebus Название: Vorticity, Statistical Mechanics, and Monte Carlo Simulation ISBN: 1441922474 ISBN-13(EAN): 9781441922472 Издательство: Springer Рейтинг: Цена: 135090.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book is drawn from across many active fields of mathematics and physics. With fresh insights into an important field, the book addresses how to access interesting, original, and publishable research in statistical modeling of large-scale flows and related fields.
Автор: Agarwal Название: Statistical Methods for Recommender Systems ISBN: 1107036070 ISBN-13(EAN): 9781107036079 Издательство: Cambridge Academ Рейтинг: Цена: 50680.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.
Автор: J. Philip Miller Название: Essential Statistical Methods for Medical Statistics, ISBN: 0444537376 ISBN-13(EAN): 9780444537379 Издательство: Elsevier Science Рейтинг: Цена: 56940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Addresses statistical challenges in epidemiological, biomedical, and pharmaceutical research. This book presents methods for assessing Biomarkers, analysis of competing risks. It offers clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs.
Автор: Ratner Bruce Название: Statistical and Machine-Learning Data Mining ISBN: 1439860912 ISBN-13(EAN): 9781439860915 Издательство: Taylor&Francis Рейтинг: Цена: 60220.00 T Наличие на складе: Нет в наличии. Описание: Rev. ed. of: Statistical modeling and analysis for database marketing. c2003.
Автор: T. W. Anderson Название: An Introduction to Multivariate Statistical Analysis, Third Edition ISBN: 0471360910 ISBN-13(EAN): 9780471360919 Издательство: Wiley Рейтинг: Цена: 184750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures. This work treats the basic and important topics in multivariate statistics.
Автор: Bradley Efron and Trevor Hastie Название: Computer Age Statistical Inference ISBN: 1107149894 ISBN-13(EAN): 9781107149892 Издательство: Cambridge Academ Рейтинг: Цена: 60190.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Автор: Becketti Название: Introduction to Time Series Using Stata ISBN: 1597181323 ISBN-13(EAN): 9781597181327 Издательство: Taylor&Francis Рейтинг: Цена: 75530.00 T Наличие на складе: Невозможна поставка. Описание: Recent decades have witnessed explosive growth in new and powerful tools for timeseries analysis. These innovations have overturned older approaches to forecasting, macroeconomic policy analysis, the study of productivity and long-run economic growth, and the trading of financial assets. Familiarity with these new tools on time series is an essential skill for statisticians, econometricians, and applied researchers. Introduction to Time Series Using Stata provides a step-by-step guide to essential timeseries techniques—from the incredibly simple to the quite complex—and, at the same time, demonstrates how these techniques can be applied in the Stata statistical package. The emphasis is on an understanding of the intuition underlying theoretical innovations and an ability to apply them. Real-world examples illustrate the application of each concept as it is introduced, and care is taken to highlight the pitfalls, as well as the power, of each new tool. Sean Becketti is a financial industry veteran with three decades of experience in academics, government, and private industry. Over the last two decades, Becketti has led proprietary research teams at several leading financial firms, responsible for the models underlying the valuation, hedging, and relative value analysis of some of the largest fixed-income portfolios in the world.
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