Computational Statistical Methodologies And Modeling For Artificial Intelligence

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

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.

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.

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.

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.

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.

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.

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.