Computational Statistical Methodologies And Modeling For Artificial Intelligence

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Computational Statistical Methodologies and Modeling for Artificial Intelligence

Priyanka Harjule,Azizur Rahman,Basant Agarwal,Vinita Tiwari
Computational Statistical Methodologies and Modeling for Artificial Intelligence Book PDF

Author : Priyanka Harjule,Azizur Rahman,Basant Agarwal,Vinita Tiwari
Publisher : CRC Press
Page : 359 pages
File Size : 43,8 Mb
Release : 2023-03-31
Category : Computers
ISBN : 9781000831092

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Computational Statistical Methodologies and Modeling for Artificial Intelligence by Priyanka Harjule,Azizur Rahman,Basant Agarwal,Vinita Tiwari Pdf

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

Radek Silhavy,Petr Silhavy,Zdenka Prokopova
Computational and Statistical Methods in Intelligent Systems Book PDF

Author : Radek Silhavy,Petr Silhavy,Zdenka Prokopova
Publisher : Unknown
Page : 386 pages
File Size : 51,8 Mb
Release : 2019
Category : COMPUTERS
ISBN : 3030002128

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Computational and Statistical Methods in Intelligent Systems by Radek Silhavy,Petr Silhavy,Zdenka Prokopova Pdf

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

Radek Silhavy,Petr Silhavy,Zdenka Prokopova
Computational Statistics and Mathematical Modeling Methods in Intelligent Systems Book PDF

Author : Radek Silhavy,Petr Silhavy,Zdenka Prokopova
Publisher : Springer Nature
Page : 424 pages
File Size : 55,5 Mb
Release : 2019-09-19
Category : Technology & Engineering
ISBN : 9783030313623

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Computational Statistics and Mathematical Modeling Methods in Intelligent Systems by Radek Silhavy,Petr Silhavy,Zdenka Prokopova Pdf

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

Samanta, Debabrata,Rao Althar, Raghavendra,Pramanik, Sabyasachi,Dutta, Soumi
Methodologies and Applications of Computational Statistics for Machine Intelligence Book PDF

Author : Samanta, Debabrata,Rao Althar, Raghavendra,Pramanik, Sabyasachi,Dutta, Soumi
Publisher : IGI Global
Page : 277 pages
File Size : 40,9 Mb
Release : 2021-06-25
Category : Computers
ISBN : 9781799877035

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Methodologies and Applications of Computational Statistics for Machine Intelligence by Samanta, Debabrata,Rao Althar, Raghavendra,Pramanik, Sabyasachi,Dutta, Soumi Pdf

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

Uwe Engel,Anabel Quan-Haase,Sunny Xun Liu,Lars Lyberg
Handbook of Computational Social Science Volume 2 Book PDF

Author : Uwe Engel,Anabel Quan-Haase,Sunny Xun Liu,Lars Lyberg
Publisher : Taylor & Francis
Page : 434 pages
File Size : 53,5 Mb
Release : 2021-11-10
Category : Computers
ISBN : 9781000448597

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Handbook of Computational Social Science, Volume 2 by Uwe Engel,Anabel Quan-Haase,Sunny Xun Liu,Lars Lyberg Pdf

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.

Computational and Statistical Methods in Intelligent Systems

Radek Silhavy,Petr Silhavy,Zdenka Prokopova
Computational and Statistical Methods in Intelligent Systems Book PDF

Author : Radek Silhavy,Petr Silhavy,Zdenka Prokopova
Publisher : Springer
Page : 386 pages
File Size : 47,8 Mb
Release : 2018-08-29
Category : Technology & Engineering
ISBN : 9783030002114

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Computational and Statistical Methods in Intelligent Systems by Radek Silhavy,Petr Silhavy,Zdenka Prokopova Pdf

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 Aspects of Model Choice

Jaromir Antoch
Computational Aspects of Model Choice Book PDF

Author : Jaromir Antoch
Publisher : Springer Science & Business Media
Page : 289 pages
File Size : 41,9 Mb
Release : 2012-12-06
Category : Business & Economics
ISBN : 9783642997662

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Computational Aspects of Model Choice by Jaromir Antoch Pdf

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.

Computationally Intensive Statistics for Intelligent IoT

Debabrata Samanta,Amit Banerjee
Computationally Intensive Statistics for Intelligent IoT Book PDF

Author : Debabrata Samanta,Amit Banerjee
Publisher : Springer Nature
Page : 233 pages
File Size : 52,6 Mb
Release : 2021-10-02
Category : Technology & Engineering
ISBN : 9789811659362

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Computationally Intensive Statistics for Intelligent IoT by Debabrata Samanta,Amit Banerjee Pdf

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

Nadja Bauer,Katja Ickstadt,Karsten Lübke,Gero Szepannek,Heike Trautmann,Maurizio Vichi
Applications in Statistical Computing Book PDF

Author : Nadja Bauer,Katja Ickstadt,Karsten Lübke,Gero Szepannek,Heike Trautmann,Maurizio Vichi
Publisher : Springer
Page : 0 pages
File Size : 52,6 Mb
Release : 2019-10-01
Category : Computers
ISBN : 3030251462

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Applications in Statistical Computing by Nadja Bauer,Katja Ickstadt,Karsten Lübke,Gero Szepannek,Heike Trautmann,Maurizio Vichi Pdf

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.

Data Analytics, Computational Statistics, and Operations Research for Engineers

Taylor & Francis Group
Data Analytics Computational Statistics and Operations Research for Engineers Book PDF

Author : Taylor & Francis Group
Publisher : CRC Press
Page : 296 pages
File Size : 43,8 Mb
Release : 2022-03-16
Category : Electronic
ISBN : 0367715112

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Data Analytics, Computational Statistics, and Operations Research for Engineers by Taylor & Francis Group Pdf

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.

Applied Modeling Techniques and Data Analysis 1

Alex Karagrigoriou,Christina Parpoula,Yannis Dimotikalis,Christos H. Skiadas
Applied Modeling Techniques and Data Analysis 1 Book PDF

Author : Alex Karagrigoriou,Christina Parpoula,Yannis Dimotikalis,Christos H. Skiadas
Publisher : John Wiley & Sons
Page : 304 pages
File Size : 45,5 Mb
Release : 2021-03-31
Category : Business & Economics
ISBN : 9781119821571

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Applied Modeling Techniques and Data Analysis 1 by Alex Karagrigoriou,Christina Parpoula,Yannis Dimotikalis,Christos H. Skiadas Pdf

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.

A Computational Approach to Statistical Learning

Taylor Arnold,Michael Kane,Bryan W. Lewis
A Computational Approach to Statistical Learning Book PDF

Author : Taylor Arnold,Michael Kane,Bryan W. Lewis
Publisher : CRC Press
Page : 362 pages
File Size : 41,6 Mb
Release : 2019-01-23
Category : Business & Economics
ISBN : 9781351694766

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A Computational Approach to Statistical Learning by Taylor Arnold,Michael Kane,Bryan W. Lewis Pdf

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.

Statistical and Machine Learning Approaches for Network Analysis

Matthias Dehmer,Subhash C. Basak
Statistical and Machine Learning Approaches for Network Analysis Book PDF

Author : Matthias Dehmer,Subhash C. Basak
Publisher : John Wiley & Sons
Page : 269 pages
File Size : 40,9 Mb
Release : 2012-06-26
Category : Mathematics
ISBN : 9781118346983

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Statistical and Machine Learning Approaches for Network Analysis by Matthias Dehmer,Subhash C. Basak Pdf

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.

Handbook of Computational Social Science, Volume 1

Uwe Engel,Anabel Quan-Haase,Sunny Liu,Lars E Lyberg
Handbook of Computational Social Science Volume 1 Book PDF

Author : Uwe Engel,Anabel Quan-Haase,Sunny Liu,Lars E Lyberg
Publisher : Taylor & Francis
Page : 417 pages
File Size : 54,6 Mb
Release : 2021-11-10
Category : Computers
ISBN : 9781000448580

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Handbook of Computational Social Science, Volume 1 by Uwe Engel,Anabel Quan-Haase,Sunny Liu,Lars E Lyberg Pdf

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 first volume focuses on the scope of computational social science, ethics, and case studies. It covers a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field but also encourages growth in 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 scientifi c and engineering sectors.

Computationally Intensive Statistics for Intelligent IoT

Debabrata Samanta,Amit Banerjee
Computationally Intensive Statistics for Intelligent IoT Book PDF

Author : Debabrata Samanta,Amit Banerjee
Publisher : Unknown
Page : 0 pages
File Size : 54,7 Mb
Release : 2021
Category : Electronic
ISBN : 9811659370

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Computationally Intensive Statistics for Intelligent IoT by Debabrata Samanta,Amit Banerjee Pdf

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.