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Author : Bala Poduval,Karly Pitman,Olga Verkhoglyadova,Peter Wintoft
Publisher : Frontiers Media SA
Page : 203 pages
File Size : 54,6 Mb
Release : 2023-04-12
Category : Science
ISBN : 9782832520581
Author : Željko Ivezić,Andrew J. Connolly,Jacob T. VanderPlas,Alexander Gray
Publisher : Princeton University Press
Page : 550 pages
File Size : 51,6 Mb
Release : 2014-01-12
Category : Science
ISBN : 9780691151687
As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers
Author : Thomas Berger,Enrico Camporeale,Bala Poduval,Veronique A. Delouille,Sophie A. Murray
Publisher : Frontiers Media SA
Page : 240 pages
File Size : 55,6 Mb
Release : 2021-11-24
Category : Science
ISBN : 9782889716715
Author : Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
Publisher : CRC Press
Page : 744 pages
File Size : 46,9 Mb
Release : 2012-03-29
Category : Computers
ISBN : 9781439841747
Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines
Author : Željko Ivezić,Andrew J. Connolly,Jacob T. VanderPlas,Alexander Gray
Publisher : Princeton University Press
Page : 548 pages
File Size : 44,8 Mb
Release : 2019-12-03
Category : Computers
ISBN : 9780691198309
"As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. The updates in this new edition will include fixing "code rot," correcting errata, and adding some new sections. In particular, the new sections include new material on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest"--
Author : Mark J. van der Laan,Sherri Rose
Publisher : Springer
Page : 640 pages
File Size : 50,8 Mb
Release : 2018-03-28
Category : Mathematics
ISBN : 9783319653044
This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generation of statisticians and data scientists. Th is book is a sequel to the first textbook on machine learning for causal inference, Targeted Learning, published in 2011. Mark van der Laan, PhD, is Jiann-Ping Hsu/Karl E. Peace Professor of Biostatistics and Statistics at UC Berkeley. His research interests include statistical methods in genomics, survival analysis, censored data, machine learning, semiparametric models, causal inference, and targeted learning. Dr. van der Laan received the 2004 Mortimer Spiegelman Award, the 2005 Van Dantzig Award, the 2005 COPSS Snedecor Award, the 2005 COPSS Presidential Award, and has graduated over 40 PhD students in biostatistics and statistics. Sherri Rose, PhD, is Associate Professor of Health Care Policy (Biostatistics) at Harvard Medical School. Her work is centered on developing and integrating innovative statistical approaches to advance human health. Dr. Rose’s methodological research focuses on nonparametric machine learning for causal inference and prediction. She co-leads the Health Policy Data Science Lab and currently serves as an associate editor for the Journal of the American Statistical Association and Biostatistics.
Author : Luis Manuel Sarro,Laurent Eyer,William O'Mullane,Joris De Ridder
Publisher : Springer Science & Business Media
Page : 259 pages
File Size : 50,9 Mb
Release : 2012-08-04
Category : Science
ISBN : 9781461433231
This volume provides an overview of the field of Astrostatistics understood as the sub-discipline dedicated to the statistical analysis of astronomical data. It presents examples of the application of the various methodologies now available to current open issues in astronomical research. The technical aspects related to the scientific analysis of the upcoming petabyte-scale databases are emphasized given the importance that scalable Knowledge Discovery techniques will have for the full exploitation of these databases. Based on the 2011 Astrostatistics and Data Mining in Large Astronomical Databases conference and school, this volume gathers examples of the work by leading authors in the areas of Astrophysics and Statistics, including a significant contribution from the various teams that prepared for the processing and analysis of the Gaia data.
Author : Mohammad Tariqul Islam
Publisher : Springer Nature
Page : 433 pages
File Size : 54,6 Mb
Release : 2024-06-13
Category : Electronic
ISBN : 9789819701421
Author : Massimo Materassi,Biagio Forte,Anthea J. Coster,Susan Skone
Publisher : Elsevier
Page : 337 pages
File Size : 47,6 Mb
Release : 2019-11-28
Category : Science
ISBN : 9780128147832
The Dynamical Ionosphere: A Systems Approach to Ionospheric Irregularity examines the Earth’s ionosphere as a dynamical system with signatures of complexity. The system is robust in its overall configuration, with smooth space-time patterns of daily, seasonal and Solar Cycle variability, but shows a hierarchy of interactions among its sub-systems, yielding apparent unpredictability, space-time irregularity, and turbulence. This interplay leads to the need for constructing realistic models of the average ionosphere, incorporating the increasing knowledge and predictability of high variability components, and for addressing the difficulty of dealing with the worst cases of ionospheric disturbances, all of which are addressed in this interdisciplinary book. Borrowing tools and techniques from classical and stochastic dynamics, information theory, signal processing, fluid dynamics and turbulence science, The Dynamical Ionosphere presents the state-of-the-art in dealing with irregularity, forecasting ionospheric threats, and theoretical interpretation of various ionospheric configurations. Presents studies addressing Earth’s ionosphere as a complex dynamical system, including irregularities and radio scintillation, ionospheric turbulence, nonlinear time series analysis, space-ionosphere connection, and space-time structures Utilizes interdisciplinary tools and techniques, such as those associated with stochastic dynamics, information theory, signal processing, fluid dynamics and turbulence science Offers new data-driven models for different ionospheric variability phenomena Provides a synoptic view of the state-of-the-art and most updated theoretical interpretation, results and data analysis tools of the "worst case" behavior in ionospheric configurations
Author : Pierre-Philippe Mathieu,Christoph Aubrecht
Publisher : Springer
Page : 328 pages
File Size : 55,9 Mb
Release : 2018-01-23
Category : Science
ISBN : 9783319656335
This book is published open access under a CC BY 4.0 license. Over the past decades, rapid developments in digital and sensing technologies, such as the Cloud, Web and Internet of Things, have dramatically changed the way we live and work. The digital transformation is revolutionizing our ability to monitor our planet and transforming the way we access, process and exploit Earth Observation data from satellites. This book reviews these megatrends and their implications for the Earth Observation community as well as the wider data economy. It provides insight into new paradigms of Open Science and Innovation applied to space data, which are characterized by openness, access to large volume of complex data, wide availability of new community tools, new techniques for big data analytics such as Artificial Intelligence, unprecedented level of computing power, and new types of collaboration among researchers, innovators, entrepreneurs and citizen scientists. In addition, this book aims to provide readers with some reflections on the future of Earth Observation, highlighting through a series of use cases not just the new opportunities created by the New Space revolution, but also the new challenges that must be addressed in order to make the most of the large volume of complex and diverse data delivered by the new generation of satellites.
Author : Enrico Camporeale,Simon Wing,Jay Johnson
Publisher : Elsevier
Page : 454 pages
File Size : 41,6 Mb
Release : 2018-05-31
Category : Science
ISBN : 9780128117897
Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields. Collects many representative non-traditional approaches to space weather into a single volume Covers, in an accessible way, the mathematical background that is not often explained in detail for space scientists Includes free software in the form of simple MATLAB® scripts that allow for replication of results in the book, also familiarizing readers with algorithms
Author : Mikhail Kanevski,Vadim Timonin,Alexi Pozdnukhov
Publisher : CRC Press
Page : 384 pages
File Size : 50,7 Mb
Release : 2009-06-09
Category : Computers
ISBN : 9780849382376
This book discusses machine learning algorithms, such as artificial neural networks of different architectures, statistical learning theory, and Support Vector Machines used for the classification and mapping of spatially distributed data. It presents basic geostatistical algorithms as well. The authors describe new trends in machine learning and their application to spatial data. The text also includes real case studies based on environmental and pollution data. It includes a CD-ROM with software that will allow both students and researchers to put the concepts to practice.
Author : Olivas, Emilio Soria,Guerrero, Jos David Mart¡n,Martinez-Sober, Marcelino,Magdalena-Benedito, Jose Rafael,Serrano L¢pez, Antonio Jos
Publisher : IGI Global
Page : 852 pages
File Size : 52,7 Mb
Release : 2009-08-31
Category : Computers
ISBN : 9781605667676
"This book investiges machine learning (ML), one of the most fruitful fields of current research, both in the proposal of new techniques and theoretic algorithms and in their application to real-life problems"--Provided by publisher.
Author : Frank Emmert-Streib,Matthias Dehmer
Publisher : Springer Science & Business Media
Page : 444 pages
File Size : 55,6 Mb
Release : 2008-11-24
Category : Computers
ISBN : 9780387848167
"Information Theory and Statistical Learning" presents theoretical and practical results about information theoretic methods used in the context of statistical learning. The book will present a comprehensive overview of the large range of different methods that have been developed in a multitude of contexts. Each chapter is written by an expert in the field. The book is intended for an interdisciplinary readership working in machine learning, applied statistics, artificial intelligence, biostatistics, computational biology, bioinformatics, web mining or related disciplines. Advance Praise for "Information Theory and Statistical Learning": "A new epoch has arrived for information sciences to integrate various disciplines such as information theory, machine learning, statistical inference, data mining, model selection etc. I am enthusiastic about recommending the present book to researchers and students, because it summarizes most of these new emerging subjects and methods, which are otherwise scattered in many places." Shun-ichi Amari, RIKEN Brain Science Institute, Professor-Emeritus at the University of Tokyo
Author : Filomena Bufano,Simone Riggi,Eva Sciacca,Francesco Schilliro
Publisher : Springer Nature
Page : 206 pages
File Size : 55,8 Mb
Release : 2023-11-15
Category : Science
ISBN : 9783031341670
This book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics.