Computational Statistical Methodologies And Modeling For Artificial Intelligence

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Computational Statistical Methodologies and Modeling for Artificial Intelligence

Author : Priyanka Harjule
Publisher : CRC Press
ISBN 13 : 1000831094
Total Pages : 359 pages
Book Rating : 4.0/5 (8 download)

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Book Synopsis Computational Statistical Methodologies and Modeling for Artificial Intelligence by : Priyanka Harjule

Download or read book Computational Statistical Methodologies and Modeling for Artificial Intelligence written by Priyanka Harjule and published by CRC Press. This book was released on 2023-03-31 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers computational statistics-based approaches for Artificial Intelligence. The aim of this book is to provide comprehensive coverage of the fundamentals through the applications of the different kinds of mathematical modelling and statistical techniques and describing their applications in different Artificial Intelligence systems. The primary users of this book will include researchers, academicians, postgraduate students, and specialists in the areas of data science, mathematical modelling, and Artificial Intelligence. It will also serve as a valuable resource for many others in the fields of electrical, computer, and optical engineering. The key features of this book are: Presents development of several real-world problem applications and experimental research in the field of computational statistics and mathematical modelling for Artificial Intelligence Examines the evolution of fundamental research into industrialized research and the transformation of applied investigation into real-time applications Examines the applications involving analytical and statistical solutions, and provides foundational and advanced concepts for beginners and industry professionals Provides a dynamic perspective to the concept of computational statistics for analysis of data and applications in intelligent systems with an objective of ensuring sustainability issues for ease of different stakeholders in various fields Integrates recent methodologies and challenges by employing mathematical modeling and statistical techniques for Artificial Intelligence

Computational and Statistical Methods in Intelligent Systems

Author : Radek Silhavy
Publisher : Springer
ISBN 13 : 303000211X
Total Pages : 386 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Computational and Statistical Methods in Intelligent Systems by : Radek Silhavy

Download or read book Computational and Statistical Methods in Intelligent Systems written by Radek Silhavy and published by Springer. This book was released on 2018-08-29 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents real-world problems and pioneering research in computational statistics, mathematical modeling, artificial intelligence and software engineering in the context of intelligent systems. It gathers the peer-reviewed proceedings of the 2nd Computational Methods in Systems and Software 2018 (CoMeSySo 2018), a conference that broke down traditional barriers by being held online. The goal of the event was to provide an international forum for discussing the latest high-quality research results.

Computational Statistics and Mathematical Modeling Methods in Intelligent Systems

Author : Radek Silhavy
Publisher : Springer Nature
ISBN 13 : 303031362X
Total Pages : 424 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Computational Statistics and Mathematical Modeling Methods in Intelligent Systems by : Radek Silhavy

Download or read book Computational Statistics and Mathematical Modeling Methods in Intelligent Systems written by Radek Silhavy and published by Springer Nature. This book was released on 2019-09-19 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents real-world problems and exploratory research in computational statistics, mathematical modeling, artificial intelligence and software engineering in the context of the intelligent systems. This book constitutes the refereed proceedings of the 3rd Computational Methods in Systems and Software 2019 (CoMeSySo 2019), a groundbreaking online conference that provides an international forum for discussing the latest high-quality research results.

Methodologies and Applications of Computational Statistics for Machine Intelligence

Author : Samanta, Debabrata
Publisher : IGI Global
ISBN 13 : 1799877035
Total Pages : 277 pages
Book Rating : 4.7/5 (998 download)

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Book Synopsis Methodologies and Applications of Computational Statistics for Machine Intelligence by : Samanta, Debabrata

Download or read book Methodologies and Applications of Computational Statistics for Machine Intelligence written by Samanta, Debabrata and published by IGI Global. This book was released on 2021-06-25 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the field of computational statistics growing rapidly, there is a need for capturing the advances and assessing their impact. Advances in simulation and graphical analysis also add to the pace of the statistical analytics field. Computational statistics play a key role in financial applications, particularly risk management and derivative pricing, biological applications including bioinformatics and computational biology, and computer network security applications that touch the lives of people. With high impacting areas such as these, it becomes important to dig deeper into the subject and explore the key areas and their progress in the recent past. Methodologies and Applications of Computational Statistics for Machine Intelligence serves as a guide to the applications of new advances in computational statistics. This text holds an accumulation of the thoughts of multiple experts together, keeping the focus on core computational statistics that apply to all domains. Covering topics including artificial intelligence, deep learning, and trend analysis, this book is an ideal resource for statisticians, computer scientists, mathematicians, lecturers, tutors, researchers, academic and corporate libraries, practitioners, professionals, students, and academicians.

Handbook of Computational Social Science, Volume 2

Author : Uwe Engel
Publisher : Taylor & Francis
ISBN 13 : 1000448592
Total Pages : 434 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Handbook of Computational Social Science, Volume 2 by : Uwe Engel

Download or read book Handbook of Computational Social Science, Volume 2 written by Uwe Engel and published by Taylor & Francis. This book was released on 2021-11-10 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.

Research Directions in Computational Mechanics

Author : National Research Council
Publisher : National Academies Press
ISBN 13 : 0309046483
Total Pages : 145 pages
Book Rating : 4.3/5 (9 download)

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Book Synopsis Research Directions in Computational Mechanics by : National Research Council

Download or read book Research Directions in Computational Mechanics written by National Research Council and published by National Academies Press. This book was released on 1991-02-01 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational mechanics is a scientific discipline that marries physics, computers, and mathematics to emulate natural physical phenomena. It is a technology that allows scientists to study and predict the performance of various productsâ€"important for research and development in the industrialized world. This book describes current trends and future research directions in computational mechanics in areas where gaps exist in current knowledge and where major advances are crucial to continued technological developments in the United States.

Computationally Intensive Statistics for Intelligent IoT

Author : Debabrata Samanta
Publisher : Springer Nature
ISBN 13 : 9811659362
Total Pages : 233 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Computationally Intensive Statistics for Intelligent IoT by : Debabrata Samanta

Download or read book Computationally Intensive Statistics for Intelligent IoT written by Debabrata Samanta and published by Springer Nature. This book was released on 2021-10-02 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book covers computational statistics, its methodologies and applications for IoT device. It includes the details in the areas of computational arithmetic and its influence on computational statistics, numerical algorithms in statistical application software, basics of computer systems, statistical techniques, linear algebra and its role in optimization techniques, evolution of optimization techniques, optimal utilization of computer resources, and statistical graphics role in data analysis. It also explores computational inferencing and computer model's role in design of experiments, Bayesian analysis, survival analysis and data mining in computational statistics.

Applications in Statistical Computing

Author : Nadja Bauer
Publisher : Springer
ISBN 13 : 9783030251468
Total Pages : 0 pages
Book Rating : 4.2/5 (514 download)

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Book Synopsis Applications in Statistical Computing by : Nadja Bauer

Download or read book Applications in Statistical Computing written by Nadja Bauer and published by Springer. This book was released on 2019-10-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents a selection of research papers on various topics at the interface of statistics and computer science. Emphasis is put on the practical applications of statistical methods in various disciplines, using machine learning and other computational methods. The book covers fields of research including the design of experiments, computational statistics, music data analysis, statistical process control, biometrics, industrial engineering, and econometrics. Gathering innovative, high-quality and scientifically relevant contributions, the volume was published in honor of Claus Weihs, Professor of Computational Statistics at TU Dortmund University, on the occasion of his 66th birthday.

Computational Aspects of Model Choice

Author : Jaromir Antoch
Publisher : Springer Science & Business Media
ISBN 13 : 364299766X
Total Pages : 289 pages
Book Rating : 4.6/5 (429 download)

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Book Synopsis Computational Aspects of Model Choice by : Jaromir Antoch

Download or read book Computational Aspects of Model Choice written by Jaromir Antoch and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although no-one is, probably, too enthused about the idea, it is a fact that the development of most empirical sciences to a great extend depends of the development of data analysis methods and techniques, which, due to the necessity of applications of computers for that pur pose, actually means that it practically depends on the advancements and orientation of computational statistics. This volume contains complete texts of the lectures held during the Summer School on "Computational Aspects of Model Choice" orga nized jointly by Charles University, Prague, and International Associa tion for Statistical Computing (IASC) on July 1-14, 1991, in Prague. Main aims of the Summer School were to review and analyse some of the recent developments concerning computational aspects of the model choice as well as their theoretical background. The topics covers the problems of the change point detection, robust estimation and its computational aspects, classification using binary trees, stochastic ap proximation and optimization including the discussion about available software, computational aspects of graphical model selection and mul tiple hypotheses testing. The bridge between these different approaches is formed by the survey paper about statistical applications of artificial intelligence.

Parsing Psychology: Statistical and Computational Methods using Physiological, Behavioral, Social, and Cognitive Data

Author : Pietro Cipresso
Publisher : Frontiers Media SA
ISBN 13 : 2889633691
Total Pages : 218 pages
Book Rating : 4.8/5 (896 download)

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Book Synopsis Parsing Psychology: Statistical and Computational Methods using Physiological, Behavioral, Social, and Cognitive Data by : Pietro Cipresso

Download or read book Parsing Psychology: Statistical and Computational Methods using Physiological, Behavioral, Social, and Cognitive Data written by Pietro Cipresso and published by Frontiers Media SA. This book was released on 2020-02-14 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.

A Computational Approach to Statistical Learning

Author : Taylor Arnold
Publisher : CRC Press
ISBN 13 : 1351694766
Total Pages : 362 pages
Book Rating : 4.3/5 (516 download)

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Book Synopsis A Computational Approach to Statistical Learning by : Taylor Arnold

Download or read book A Computational Approach to Statistical Learning written by Taylor Arnold and published by CRC Press. This book was released on 2019-01-23 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. Taylor Arnold is an assistant professor of statistics at the University of Richmond. His work at the intersection of computer vision, natural language processing, and digital humanities has been supported by multiple grants from the National Endowment for the Humanities (NEH) and the American Council of Learned Societies (ACLS). His first book, Humanities Data in R, was published in 2015. Michael Kane is an assistant professor of biostatistics at Yale University. He is the recipient of grants from the National Institutes of Health (NIH), DARPA, and the Bill and Melinda Gates Foundation. His R package bigmemory won the Chamber's prize for statistical software in 2010. Bryan Lewis is an applied mathematician and author of many popular R packages, including irlba, doRedis, and threejs.

Applied Modeling Techniques and Data Analysis 1

Author : Yiannis Dimotikalis
Publisher : John Wiley & Sons
ISBN 13 : 1786306735
Total Pages : 306 pages
Book Rating : 4.7/5 (863 download)

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Book Synopsis Applied Modeling Techniques and Data Analysis 1 by : Yiannis Dimotikalis

Download or read book Applied Modeling Techniques and Data Analysis 1 written by Yiannis Dimotikalis and published by John Wiley & Sons. This book was released on 2021-05-11 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically. This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 1 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.

Data Analytics, Computational Statistics, and Operations Research for Engineers

Author : Taylor & Francis Group
Publisher : CRC Press
ISBN 13 : 9780367715113
Total Pages : 296 pages
Book Rating : 4.7/5 (151 download)

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Book Synopsis Data Analytics, Computational Statistics, and Operations Research for Engineers by : Taylor & Francis Group

Download or read book Data Analytics, Computational Statistics, and Operations Research for Engineers written by Taylor & Francis Group and published by CRC Press. This book was released on 2022-03-16 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements.

Statistical and Machine Learning Approaches for Network Analysis

Author : Matthias Dehmer
Publisher : John Wiley & Sons
ISBN 13 : 111834698X
Total Pages : 269 pages
Book Rating : 4.1/5 (183 download)

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Book Synopsis Statistical and Machine Learning Approaches for Network Analysis by : Matthias Dehmer

Download or read book Statistical and Machine Learning Approaches for Network Analysis written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2012-06-26 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internationally renowned researchers in the field of interdisciplinary network theory, the book presents current and classical methods to analyze networks statistically. Methods from machine learning, data mining, and information theory are strongly emphasized throughout. Real data sets are used to showcase the discussed methods and topics, which include: A survey of computational approaches to reconstruct and partition biological networks An introduction to complex networks—measures, statistical properties, and models Modeling for evolving biological networks The structure of an evolving random bipartite graph Density-based enumeration in structured data Hyponym extraction employing a weighted graph kernel Statistical and Machine Learning Approaches for Network Analysis is an excellent supplemental text for graduate-level, cross-disciplinary courses in applied discrete mathematics, bioinformatics, pattern recognition, and computer science. The book is also a valuable reference for researchers and practitioners in the fields of applied discrete mathematics, machine learning, data mining, and biostatistics.

Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications

Author : K. G. Srinivasa
Publisher : Springer Nature
ISBN 13 : 9811524459
Total Pages : 318 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications by : K. G. Srinivasa

Download or read book Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications written by K. G. Srinivasa and published by Springer Nature. This book was released on 2020-01-30 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics. It also highlights the role of computing and machine learning in knowledge extraction from biological data, and how this knowledge can be applied in fields such as drug design, health supplements, gene therapy, proteomics and agriculture.

Statistical Modeling and Machine Learning for Molecular Biology

Author : Alan Moses
Publisher : CRC Press
ISBN 13 : 1482258609
Total Pages : 264 pages
Book Rating : 4.4/5 (822 download)

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Book Synopsis Statistical Modeling and Machine Learning for Molecular Biology by : Alan Moses

Download or read book Statistical Modeling and Machine Learning for Molecular Biology written by Alan Moses and published by CRC Press. This book was released on 2017-01-06 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Molecular biologists are performing increasingly large and complicated experiments, but often have little background in data analysis. The book is devoted to teaching the statistical and computational techniques molecular biologists need to analyze their data. It explains the big-picture concepts in data analysis using a wide variety of real-world molecular biological examples such as eQTLs, ortholog identification, motif finding, inference of population structure, protein fold prediction and many more. The book takes a pragmatic approach, focusing on techniques that are based on elegant mathematics yet are the simplest to explain to scientists with little background in computers and statistics.

Statistical Relational Artificial Intelligence

Author : Luc De Raedt
Publisher : Morgan & Claypool Publishers
ISBN 13 : 1681731800
Total Pages : 259 pages
Book Rating : 4.6/5 (817 download)

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Book Synopsis Statistical Relational Artificial Intelligence by : Luc De Raedt

Download or read book Statistical Relational Artificial Intelligence written by Luc De Raedt and published by Morgan & Claypool Publishers. This book was released on 2016-03-24 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.