Concentration of Maxima and Fundamental Limits in High-Dimensional Testing and Inference - 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 Concentration of Maxima and Fundamental Limits in High-Dimensional Testing and Inference write by Zheng Gao. This book was released on 2021-09-07. Concentration of Maxima and Fundamental Limits in High-Dimensional Testing and Inference available in PDF, EPUB and Kindle. This book provides a unified exposition of some fundamental theoretical problems in high-dimensional statistics. It specifically considers the canonical problems of detection and support estimation for sparse signals observed with noise. Novel phase-transition results are obtained for the signal support estimation problem under a variety of statistical risks. Based on a surprising connection to a concentration of maxima probabilistic phenomenon, the authors obtain a complete characterization of the exact support recovery problem for thresholding estimators under dependent errors.
High-Dimensional Probability
High-Dimensional Probability - 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 High-Dimensional Probability write by Roman Vershynin. This book was released on 2018-09-27. High-Dimensional Probability available in PDF, EPUB and Kindle. An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.
Introduction to High-Dimensional Statistics
Introduction to High-Dimensional Statistics - 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 Introduction to High-Dimensional Statistics write by Christophe Giraud. This book was released on 2021-08-25. Introduction to High-Dimensional Statistics available in PDF, EPUB and Kindle. Praise for the first edition: "[This book] succeeds singularly at providing a structured introduction to this active field of research. ... it is arguably the most accessible overview yet published of the mathematical ideas and principles that one needs to master to enter the field of high-dimensional statistics. ... recommended to anyone interested in the main results of current research in high-dimensional statistics as well as anyone interested in acquiring the core mathematical skills to enter this area of research." —Journal of the American Statistical Association Introduction to High-Dimensional Statistics, Second Edition preserves the philosophy of the first edition: to be a concise guide for students and researchers discovering the area and interested in the mathematics involved. The main concepts and ideas are presented in simple settings, avoiding thereby unessential technicalities. High-dimensional statistics is a fast-evolving field, and much progress has been made on a large variety of topics, providing new insights and methods. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this new edition: Offers revised chapters from the previous edition, with the inclusion of many additional materials on some important topics, including compress sensing, estimation with convex constraints, the slope estimator, simultaneously low-rank and row-sparse linear regression, or aggregation of a continuous set of estimators. Introduces three new chapters on iterative algorithms, clustering, and minimax lower bounds. Provides enhanced appendices, minimax lower-bounds mainly with the addition of the Davis-Kahan perturbation bound and of two simple versions of the Hanson-Wright concentration inequality. Covers cutting-edge statistical methods including model selection, sparsity and the Lasso, iterative hard thresholding, aggregation, support vector machines, and learning theory. Provides detailed exercises at the end of every chapter with collaborative solutions on a wiki site. Illustrates concepts with simple but clear practical examples.
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics - 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 Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics write by Christine Sinoquet. This book was released on 2014-09-18. Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics available in PDF, EPUB and Kindle. Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity. These data will only allow insights into this wealth of so-called 'omics' data if represented by flexible and scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent a powerful formalism to discover complex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics in the broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest. A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the design of advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes: (1) Gene network inference (2) Causality discovery (3) Association genetics (4) Epigenetics (5) Detection of copy number variations (6) Prediction of outcomes from high-dimensional genomic data. Written by leading international experts, this is a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerful techniques.
Bulletin of the Atomic Scientists
Bulletin of the Atomic Scientists - 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 Bulletin of the Atomic Scientists write by . This book was released on 1969-02. Bulletin of the Atomic Scientists available in PDF, EPUB and Kindle. The Bulletin of the Atomic Scientists is the premier public resource on scientific and technological developments that impact global security. Founded by Manhattan Project Scientists, the Bulletin's iconic "Doomsday Clock" stimulates solutions for a safer world.
