Trends In Mathematical Information And Data Sciences
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Author |
: Narayanaswamy Balakrishnan |
Publisher |
: Springer Nature |
Total Pages |
: 450 |
Release |
: 2022-06-27 |
ISBN-10 |
: 9783031041372 |
ISBN-13 |
: 3031041372 |
Rating |
: 4/5 (72 Downloads) |
Synopsis Trends in Mathematical, Information and Data Sciences by : Narayanaswamy Balakrishnan
This book involves ideas/results from the topics of mathematical, information, and data sciences, in connection with the main research interests of Professor Pardo that can be summarized as Information Theory with Applications to Statistical Inference. This book is a tribute to Professor Leandro Pardo, who has chaired the Department of Statistics and OR of the Complutense University in Madrid, and he has been also President of the Spanish Society of Statistics and Operations Research. In this way, the contributions have been structured into three parts, which often overlap to a greater or lesser extent, namely Trends in Mathematical Sciences (Part I) Trends in Information Sciences (Part II) Trends in Data Sciences (Part III) The contributions gathered in this book have offered either new developments from a theoretical and/or computational and/or applied point of view, or reviews of recent literature of outstanding developments. They have been applied through nice examples in climatology, chemistry, economics, engineering, geology, health sciences, physics, pandemics, and socioeconomic indicators. Consequently, the intended audience of this book is mainly statisticians, mathematicians, computer scientists, and so on, but users of these disciplines as well as experts in the involved applications may certainly find this book a very interesting read.
Author |
: Luis A. García-Escudero |
Publisher |
: Springer Nature |
Total Pages |
: 421 |
Release |
: 2022-08-24 |
ISBN-10 |
: 9783031155093 |
ISBN-13 |
: 3031155092 |
Rating |
: 4/5 (93 Downloads) |
Synopsis Building Bridges between Soft and Statistical Methodologies for Data Science by : Luis A. García-Escudero
Nowadays, data analysis is becoming an appealing topic due to the emergence of new data types, dimensions, and sources. This motivates the development of probabilistic/statistical approaches and tools to cope with these data. Different communities of experts, namely statisticians, mathematicians, computer scientists, engineers, econometricians, and psychologists are more and more interested in facing this challenge. As a consequence, there is a clear need to build bridges between all these communities for Data Science. This book contains more than fifty selected recent contributions aiming to establish the above referred bridges. These contributions address very different and relevant aspects such as imprecise probabilities, information theory, random sets and random fuzzy sets, belief functions, possibility theory, dependence modelling and copulas, clustering, depth concepts, dimensionality reduction of complex data and robustness.
Author |
: Dirk P. Kroese |
Publisher |
: CRC Press |
Total Pages |
: 538 |
Release |
: 2019-11-20 |
ISBN-10 |
: 9781000730777 |
ISBN-13 |
: 1000730778 |
Rating |
: 4/5 (77 Downloads) |
Synopsis Data Science and Machine Learning by : Dirk P. Kroese
Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
Author |
: Narayanaswamy Balakrishnan |
Publisher |
: |
Total Pages |
: 0 |
Release |
: 2023 |
ISBN-10 |
: 3031041380 |
ISBN-13 |
: 9783031041389 |
Rating |
: 4/5 (80 Downloads) |
Synopsis Trends in Mathematical, Information and Data Sciences by : Narayanaswamy Balakrishnan
This book involves ideas/results from the topics of mathematical, information, and data sciences, in connection with the main research interests of Professor Pardo that can be summarized as Information Theory with Applications to Statistical Inference. This book is a tribute to Professor Leandro Pardo, who has chaired the Department of Statistics and OR of the Complutense University in Madrid, and he has been also President of the Spanish Society of Statistics and Operations Research. In this way, the contributions have been structured into three parts, which often overlap to a greater or lesser extent, namely Trends in Mathematical Sciences (Part I) Trends in Information Sciences (Part II) Trends in Data Sciences (Part III) The contributions gathered in this book have offered either new developments from a theoretical and/or computational and/or applied point of view, or reviews of recent literature of outstanding developments. They have been applied through nice examples in climatology, chemistry, economics, engineering, geology, health sciences, physics, pandemics, and socioeconomic indicators. Consequently, the intended audience of this book is mainly statisticians, mathematicians, computer scientists, and so on, but users of these disciplines as well as experts in the involved applications may certainly find this book a very interesting read.
Author |
: Biswadip Basu Mallik |
Publisher |
: Bentham Science Publishers |
Total Pages |
: 223 |
Release |
: 2023-08-24 |
ISBN-10 |
: 9789815124859 |
ISBN-13 |
: 9815124854 |
Rating |
: 4/5 (59 Downloads) |
Synopsis Advanced Mathematical Applications in Data Science by : Biswadip Basu Mallik
Advanced Mathematical Applications in Data Science comprehensively explores the crucial role mathematics plays in the field of data science. Each chapter is contributed by scientists, researchers, and academicians. The 13 chapters cover a range of mathematical concepts utilized in data science, enabling readers to understand the intricate connection between mathematics and data analysis. The book covers diverse topics, including, machine learning models, the Kalman filter, data modeling, artificial neural networks, clustering techniques, and more, showcasing the application of advanced mathematical tools for effective data processing and analysis. With a strong emphasis on real-world applications, the book offers a deeper understanding of the foundational principles behind data analysis and its numerous interdisciplinary applications. This reference is an invaluable resource for graduate students, researchers, academicians, and learners pursuing a research career in mathematical computing or completing advanced data science courses. Key Features: Comprehensive coverage of advanced mathematical concepts and techniques in data science Contributions from established scientists, researchers, and academicians Real-world case studies and practical applications of mathematical methods Focus on diverse areas, such as image classification, carbon emission assessment, customer churn prediction, and healthcare data analysis In-depth exploration of data science's connection with mathematics, computer science, and artificial intelligence Scholarly references for each chapter Suitable for readers with high school-level mathematical knowledge, making it accessible to a broad audience in academia and industry.
Author |
: Vinai K. Singh |
Publisher |
: Springer Nature |
Total Pages |
: 441 |
Release |
: 2021-08-23 |
ISBN-10 |
: 9783030682811 |
ISBN-13 |
: 3030682811 |
Rating |
: 4/5 (11 Downloads) |
Synopsis Recent Trends in Mathematical Modeling and High Performance Computing by : Vinai K. Singh
This volume explores the connections between mathematical modeling, computational methods, and high performance computing, and how recent developments in these areas can help to solve complex problems in the natural sciences and engineering. The content of the book is based on talks and papers presented at the conference Modern Mathematical Methods and High Performance Computing in Science & Technology (M3HPCST), held at Inderprastha Engineering College in Ghaziabad, India in January 2020. A wide range of both theoretical and applied topics are covered in detail, including the conceptualization of infinity, efficient domain decomposition, high capacity wireless communication, infectious disease modeling, and more. These chapters are organized around the following areas: Partial and ordinary differential equations Optimization and optimal control High performance and scientific computing Stochastic models and statistics Recent Trends in Mathematical Modeling and High Performance Computing will be of interest to researchers in both mathematics and engineering, as well as to practitioners who face complex models and extensive computations.
Author |
: Miguel R. D. Rodrigues |
Publisher |
: Cambridge University Press |
Total Pages |
: 561 |
Release |
: 2021-04-08 |
ISBN-10 |
: 9781108427135 |
ISBN-13 |
: 1108427138 |
Rating |
: 4/5 (35 Downloads) |
Synopsis Information-Theoretic Methods in Data Science by : Miguel R. D. Rodrigues
The first unified treatment of the interface between information theory and emerging topics in data science, written in a clear, tutorial style. Covering topics such as data acquisition, representation, analysis, and communication, it is ideal for graduate students and researchers in information theory, signal processing, and machine learning.
Author |
: Li M. Chen |
Publisher |
: Springer |
Total Pages |
: 219 |
Release |
: 2015-12-15 |
ISBN-10 |
: 9783319251271 |
ISBN-13 |
: 3319251279 |
Rating |
: 4/5 (71 Downloads) |
Synopsis Mathematical Problems in Data Science by : Li M. Chen
This book describes current problems in data science and Big Data. Key topics are data classification, Graph Cut, the Laplacian Matrix, Google Page Rank, efficient algorithms, hardness of problems, different types of big data, geometric data structures, topological data processing, and various learning methods. For unsolved problems such as incomplete data relation and reconstruction, the book includes possible solutions and both statistical and computational methods for data analysis. Initial chapters focus on exploring the properties of incomplete data sets and partial-connectedness among data points or data sets. Discussions also cover the completion problem of Netflix matrix; machine learning method on massive data sets; image segmentation and video search. This book introduces software tools for data science and Big Data such MapReduce, Hadoop, and Spark. This book contains three parts. The first part explores the fundamental tools of data science. It includes basic graph theoretical methods, statistical and AI methods for massive data sets. In second part, chapters focus on the procedural treatment of data science problems including machine learning methods, mathematical image and video processing, topological data analysis, and statistical methods. The final section provides case studies on special topics in variational learning, manifold learning, business and financial data rec overy, geometric search, and computing models. Mathematical Problems in Data Science is a valuable resource for researchers and professionals working in data science, information systems and networks. Advanced-level students studying computer science, electrical engineering and mathematics will also find the content helpful.
Author |
: Hemen Dutta |
Publisher |
: Springer Nature |
Total Pages |
: 912 |
Release |
: 2019-08-23 |
ISBN-10 |
: 9783030152420 |
ISBN-13 |
: 3030152421 |
Rating |
: 4/5 (20 Downloads) |
Synopsis Current Trends in Mathematical Analysis and Its Interdisciplinary Applications by : Hemen Dutta
This book explores several important aspects of recent developments in the interdisciplinary applications of mathematical analysis (MA), and highlights how MA is now being employed in many areas of scientific research. Each of the 23 carefully reviewed chapters was written by experienced expert(s) in respective field, and will enrich readers’ understanding of the respective research problems, providing them with sufficient background to understand the theories, methods and applications discussed. The book’s main goal is to highlight the latest trends and advances, equipping interested readers to pursue further research of their own. Given its scope, the book will especially benefit graduate and PhD students, researchers in the applied sciences, educators, and engineers with an interest in recent developments in the interdisciplinary applications of mathematical analysis.
Author |
: D. Marc Kilgour |
Publisher |
: Springer Nature |
Total Pages |
: 728 |
Release |
: 2021-08-29 |
ISBN-10 |
: 9783030635916 |
ISBN-13 |
: 3030635910 |
Rating |
: 4/5 (16 Downloads) |
Synopsis Recent Developments in Mathematical, Statistical and Computational Sciences by : D. Marc Kilgour
This book constitutes an up-to-date account of principles, methods, and tools for mathematical and statistical modelling in a wide range of research fields, including medicine, health sciences, biology, environmental science, engineering, physics, chemistry, computation, finance, economics, and social sciences. It presents original solutions to real-world problems, emphasizes the coordinated development of theories and applications, and promotes interdisciplinary collaboration among mathematicians, statisticians, and researchers in other disciplines. Based on a highly successful meeting, the International Conference on Applied Mathematics, Modeling and Computational Science, AMMCS 2019, held from August 18 to 23, 2019, on the main campus of Wilfrid Laurier University, Waterloo, Canada, the contributions are the results of submissions from the conference participants. They provide readers with a broader view of the methods, ideas and tools used in mathematical, statistical and computational sciences.