Large Scale Global And Simultaneous Inference
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| Author | : Bradley Efron |
| Publisher | : Cambridge University Press |
| Total Pages | : |
| Release | : 2012-11-29 |
| Genre | : Mathematics |
| ISBN | : 1139492136 |
We live in a new age for statistical inference, where modern scientific technology such as microarrays and fMRI machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Doing thousands of problems at once is more than repeated application of classical methods. Taking an empirical Bayes approach, Bradley Efron, inventor of the bootstrap, shows how information accrues across problems in a way that combines Bayesian and frequentist ideas. Estimation, testing and prediction blend in this framework, producing opportunities for new methodologies of increased power. New difficulties also arise, easily leading to flawed inferences. This book takes a careful look at both the promise and pitfalls of large-scale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. Emphasis is on the inferential ideas underlying technical developments, illustrated using a large number of real examples.
| Author | : Bradley Efron |
| Publisher | : Cambridge University Press |
| Total Pages | : 496 |
| Release | : 2016-07-21 |
| Genre | : Mathematics |
| ISBN | : 1108107958 |
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.
| Author | : Samuel Kotz |
| Publisher | : Cambridge University Press |
| Total Pages | : 296 |
| Release | : 2004-02-16 |
| Genre | : Mathematics |
| ISBN | : 9780521826549 |
Almost all the results available in the literature on multivariate t-distributions published in the last 50 years are now collected together in this comprehensive reference. Because these distributions are becoming more prominent in many applications, this book is a must for any serious researcher or consultant working in multivariate analysis and statistical distributions. Much of this material has never before appeared in book form. The first part of the book emphasizes theoretical results of a probabilistic nature. In the second part of the book, these are supplemented by a variety of statistical aspects. Various generalizations and applications are dealt with in the final chapters. The material on estimation and regression models is of special value for practitioners in statistics and economics. A comprehensive bibliography of over 350 references is included.
| Author | : Frank Dellaert |
| Publisher | : Springer |
| Total Pages | : 452 |
| Release | : 2012-09-22 |
| Genre | : Computers |
| ISBN | : 3642340911 |
This book constitutes the thoroughly refereed post-proceedings of the 15th International Workshop on Theoretic Foundations of Computer Vision, held as a Dagstuhl Seminar in Dagstuhl Castle, Germany, in June/July 2011. The 19 revised full papers presented were carefully reviewed and selected after a blind peer-review process. The topic of this Workshop was Outdoor and Large-Scale Real-World Scene Analysis, which covers all aspects, applications and open problems regarding the performance or design of computer vision algorithms capable of working in outdoor setups and/or large-scale environments. Developing these methods is important for driver assistance, city modeling and reconstruction, virtual tourism, telepresence, and motion capture.
| Author | : Frank Emmert-Streib |
| Publisher | : Springer Nature |
| Total Pages | : 582 |
| Release | : 2023-10-03 |
| Genre | : Technology & Engineering |
| ISBN | : 3031133390 |
The textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples.
| Author | : Bradley Efron |
| Publisher | : Cambridge University Press |
| Total Pages | : 514 |
| Release | : 2021-06-17 |
| Genre | : Mathematics |
| ISBN | : 1108915876 |
The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. '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? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. 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. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.
| Author | : El-Ghazali Talbi |
| Publisher | : John Wiley & Sons |
| Total Pages | : 400 |
| Release | : 2007-12-04 |
| Genre | : Computers |
| ISBN | : 9780470191620 |
The only single, up-to-date source for Grid issues in bioinformatics and biology Bioinformatics is fast emerging as an important discipline for academic research and industrial applications, creating a need for the use of Grid computing techniques for large-scale distributed applications. This book successfully presents Grid algorithms and their real-world applications, provides details on modern and ongoing research, and explores software frameworks that integrate bioinformatics and computational biology. Additional coverage includes: * Bio-ontology and data mining * Data visualization * DNA assembly, clustering, and mapping * Molecular evolution and phylogeny * Gene expression and micro-arrays * Molecular modeling and simulation * Sequence search and alignment * Protein structure prediction * Grid infrastructure, middleware, and tools for bio data Grid Computing for Bioinformatics and Computational Biology is an indispensable resource for professionals in several research and development communities including bioinformatics, computational biology, Grid computing, data mining, and more. It also serves as an ideal textbook for undergraduate- and graduate-level courses in bioinformatics and Grid computing.
| Author | : Gary King |
| Publisher | : Cambridge University Press |
| Total Pages | : 436 |
| Release | : 2004-09-13 |
| Genre | : Nature |
| ISBN | : 9780521542807 |
Drawing upon the recent explosion of research in the field, a diverse group of scholars surveys the latest strategies for solving ecological inference problems, the process of trying to infer individual behavior from aggregate data. The uncertainties and information lost in aggregation make ecological inference one of the most difficult areas of statistical inference, but these inferences are required in many academic fields, as well as by legislatures and the Courts in redistricting, marketing research by business, and policy analysis by governments. This wide-ranging collection of essays offers many fresh and important contributions to the study of ecological inference.
| Author | : Bradley Efron |
| Publisher | : |
| Total Pages | : 22 |
| Release | : 2003 |
| Genre | : |
| ISBN | : |
| Author | : Yuichi Ikeda |
| Publisher | : Springer Nature |
| Total Pages | : 509 |
| Release | : 2021-06-12 |
| Genre | : Business & Economics |
| ISBN | : 9811549443 |
In this book, the authors analyze big data on global interdependence caused by the flows of commodities, money, and people, using a network science approach to obtain differing views of globalization and to clarify the facts on isolation of communities. Globalization reduces international economic inequality, i.e., it allows emerging countries to catch up while it increases relative poverty in some advanced countries. How should this trade-off between international and domestic inequalities be resolved? At the same time, the reduction of biocultural diversity caused by globalization needs to be avoided. What kind of change is required in local communities to conserve biocultural diversity? On the issue of commodity flow, research results of the supply-chain network, isolation in industry, and resource flows and stocks are presented in this book. For monetary flow, ownership networks, value-added networks, and profit shifting were studied; and regarding the flow of people, linkage of ethnic groups, immigrant assimilation, and refugees were examined. Based on the resulting view of globalization and isolation, the development of the isolation index using machine learning is discussed. Finally, recommendations for evidence-based policymaking in the United Nations are considered.
