Complex Models And Computational Methods In Statistics
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Complex Models And Computational Methods In Statistics
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Author : Matteo Grigoletto
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-01-26
Complex Models And Computational Methods In Statistics written by Matteo Grigoletto and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-01-26 with Mathematics categories.
The use of computational methods in statistics to face complex problems and highly dimensional data, as well as the widespread availability of computer technology, is no news. The range of applications, instead, is unprecedented. As often occurs, new and complex data types require new strategies, demanding for the development of novel statistical methods and suggesting stimulating mathematical problems. This book is addressed to researchers working at the forefront of the statistical analysis of complex systems and using computationally intensive statistical methods.
Computational And Statistical Methods For Analysing Big Data With Applications
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Author : Shen Liu
language : en
Publisher: Academic Press
Release Date : 2015-11-20
Computational And Statistical Methods For Analysing Big Data With Applications written by Shen Liu and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-20 with Mathematics categories.
Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data. - Advanced computational and statistical methodologies for analysing big data are developed - Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable - Case studies are discussed to demonstrate the implementation of the developed methods - Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation - Computing code/programs are provided where appropriate
Spatial Statistics And Computational Methods
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Author : Jesper Møller
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-04-17
Spatial Statistics And Computational Methods written by Jesper Møller and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-17 with Mathematics categories.
Spatial statistics and Markov Chain Monte Carlo (MCMC) techniques have each undergone major developments in the last decade. Also, these two areas are mutually reinforcing, because MCMC methods are often necessary for the practical implementation of spatial statistical inference, while new spatial stochastic models in turn motivate the development of improved MCMC algorithms. This volume shows how sophisticated spatial statistical and computational methods apply to a range of problems of increasing importance for applications in science and technology. It consists of four chapters: 1. Petros Dellaportas and Gareth O. Roberts give a tutorial on MCMC methods, the computational methodology which is essential for virtually all the complex spatial models to be considered in subsequent chapters. 2. Peter J. Diggle, Paulo J, Ribeiro Jr., and Ole F. Christensen introduce the reader to the model- based approach to geostatistics, i.e. the application of general statistical principles to the formulation of explicit stochastic models for geostatistical data, and to inference within a declared class of models. 3. Merrilee A. Hurn, Oddvar K. Husby, and H?vard Rue discuss various aspects of image analysis, ranging from low to high level tasks, and illustrated with different examples of applications. 4. Jesper Moller and Rasmus P. Waggepetersen collect recent theoretical advances in simulation-based inference for spatial point processes, and discuss some examples of applications. The volume introduces topics of current interest in spatial and computational statistics, which should be accessible to postgraduate students as well as to experienced statistical researchers. It is partly based on the course material for the "TMR and MaPhySto Summer School on Spatial Statistics and Computational Methods," held at Aalborg University, Denmark, August 19-22, 2001.
Methods Of Mathematical Modelling And Computation For Complex Systems
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Author : Jagdev Singh
language : en
Publisher: Springer Nature
Release Date : 2021-08-26
Methods Of Mathematical Modelling And Computation For Complex Systems written by Jagdev Singh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-26 with Technology & Engineering categories.
This book contains several contemporary topics in the areas of mathematical modelling and computation for complex systems. The readers find several new mathematical methods, mathematical models and computational techniques having significant relevance in studying various complex systems. The chapters aim to enrich the understanding of topics presented by carefully discussing the associated problems and issues, possible solutions and their applications or relevance in other scientific areas of study and research. The book is a valuable resource for graduate students, researchers and educators in understanding and studying various new aspects associated with complex systems. Key Feature • The chapters include theory and application in a mix and balanced way. • Readers find reasonable details of developments concerning a topic included in this book. • The text is emphasized to present in self-contained manner with inclusion of new research problems and questions.
Computational Methods For Data Analysis
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Author : Yeliz Karaca
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2018-12-17
Computational Methods For Data Analysis written by Yeliz Karaca and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-17 with Mathematics categories.
This graduate text covers a variety of mathematical and statistical tools for the analysis of big data coming from biology, medicine and economics. Neural networks, Markov chains, tools from statistical physics and wavelet analysis are used to develop efficient computational algorithms, which are then used for the processing of real-life data using Matlab.
Computational Methods For Inverse Problems
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Author : Curtis R. Vogel
language : en
Publisher: SIAM
Release Date : 2002-01-01
Computational Methods For Inverse Problems written by Curtis R. Vogel and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-01-01 with Mathematics categories.
Provides a basic understanding of both the underlying mathematics and the computational methods used to solve inverse problems.
Advances In Complex Data Modeling And Computational Methods In Statistics
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Author : Anna Maria Paganoni
language : en
Publisher: Springer
Release Date : 2014-11-04
Advances In Complex Data Modeling And Computational Methods In Statistics written by Anna Maria Paganoni and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-04 with Mathematics categories.
The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.
Data Driven Modeling Scientific Computation
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Author : Jose Nathan Kutz
language : en
Publisher:
Release Date : 2013-08-08
Data Driven Modeling Scientific Computation written by Jose Nathan Kutz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-08-08 with Computers categories.
Combining scientific computing methods and algorithms with modern data analysis techniques, including basic applications of compressive sensing and machine learning, this book develops techniques that allow for the integration of the dynamics of complex systems and big data. MATLAB is used throughout for mathematical solution strategies.
Computational Modeling
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Author : Charles S. Taber
language : en
Publisher: SAGE
Release Date : 1996-03-21
Computational Modeling written by Charles S. Taber and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-03-21 with Computers categories.
Computational modelling allows researchers to combine the rich detail of qualitative research with the rigour of quantitative and formal research, as well as to represent complex structures and processes within a theoretical model. After an introduction to modelling, the authors discuss the role of computational methods in the social sciences. They treat computational methods, including dynamic simulation, knowledge-based models and machine learning, as a single broad class of research tools and develop a framework for incorporating them within established traditions of social science research. They provide a concise description of each method and a variety of social science illustrations, including four detailed examples.
Statistical And Computational Methods In Brain Image Analysis
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Author : Moo K. Chung
language : en
Publisher: CRC Press
Release Date : 2013-07-23
Statistical And Computational Methods In Brain Image Analysis written by Moo K. Chung and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-23 with Mathematics categories.
The massive amount of nonstandard high-dimensional brain imaging data being generated is often difficult to analyze using current techniques. This challenge in brain image analysis requires new computational approaches and solutions. But none of the research papers or books in the field describe the quantitative techniques with detailed illustrations of actual imaging data and computer codes. Using MATLAB® and case study data sets, Statistical and Computational Methods in Brain Image Analysis is the first book to explicitly explain how to perform statistical analysis on brain imaging data. The book focuses on methodological issues in analyzing structural brain imaging modalities such as MRI and DTI. Real imaging applications and examples elucidate the concepts and methods. In addition, most of the brain imaging data sets and MATLAB codes are available on the author’s website. By supplying the data and codes, this book enables researchers to start their statistical analyses immediately. Also suitable for graduate students, it provides an understanding of the various statistical and computational methodologies used in the field as well as important and technically challenging topics.