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Bayesian Inference on Complicated Data
Due to great applications in various fields, such as social science, biomedicine, genomics, and signal processing, and the improvement of computing ability, Bayesian inference has made substantial developments for analyzing complicated data. This book introduces key ideas of Bayesian sampling methods, Bayesian estimation, and selection of the prior. It is structured around topics on the impact of the choice of the prior on Bayesian statistics, some advances on Bayesian sampling methods, and Bayesian inference for complicated data including breast cancer data, cloud-based healthcare data, gene network data, and longitudinal data. This volume is designed for statisticians, engineers, doctors, and machine learning researchers.
Bayesian Inference
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Data Clustering
In view of the considerable applications of data clustering techniques in various fields, such as engineering, artificial intelligence, machine learning, clinical medicine, biology, ecology, disease diagnosis, and business marketing, many data clustering algorithms and methods have been developed to deal with complicated data. These techniques include supervised learning methods and unsupervised learning methods such as density-based clustering, K-means clustering, and K-nearest neighbor clustering. This book reviews recently developed data clustering techniques and algorithms and discusses the development of data clustering, including measures of similarity or dissimilarity for data clustering, data clustering algorithms, assessment of clustering algorithms, and data clustering methods recently developed for insurance, psychology, pattern recognition, and survey data.
New Insights into Bayesian Inference
This book is an introduction to the mathematical analysis of Bayesian decision-making when the state of the problem is unknown but further data about it can be obtained. The objective of such analysis is to determine the optimal decision or solution that is logically consistent with the preferences of the decision-maker, that can be analyzed using numerical utilities or criteria with the probabilities assigned to the possible state of the problem, such that these probabilities are updated by gathering new information.
Frontiers of Statistical Decision Making and Bayesian Analysis
Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical...
Statistical Methods in Data Science and Applications
The rise of big data has significantly elevated the significance of data science, catalyzing extensive research across multiple fields, including mathematics, statistics, computer science, and artificial intelligence. Data science encompasses modeling, computation, and learning processes to transform data into information, information into knowledge, and knowledge into actionable decisions. However, the intricacies of big data pose numerous challenges, such as dealing with missing data, high- and ultra-high-dimensional data, response dependencies, time series analysis, and distributed storage. Existing theories, methods, and algorithms for analyzing big data encounter significant hurdles, es...
Methods and Applications of Autonomous Experimentation
Autonomous Experimentation is poised to revolutionize scientific experiments at advanced experimental facilities. Whereas previously, human experimenters were burdened with the laborious task of overseeing each measurement, recent advances in mathematics, machine learning and algorithms have alleviated this burden by enabling automated and intelligent decision-making, minimizing the need for human interference. Illustrating theoretical foundations and incorporating practitioners’ first-hand experiences, this book is a practical guide to successful Autonomous Experimentation. Despite the field’s growing potential, there exists numerous myths and misconceptions surrounding Autonomous Experimentation. Combining insights from theorists, machine-learning engineers and applied scientists, this book aims to lay the foundation for future research and widespread adoption within the scientific community. This book is particularly useful for members of the scientific community looking to improve their research methods but also contains additional insights for students and industry professionals interested in the future of the field.
Advances in Water Resources & Hydraulic Engineering
"Advances in Water Resources and Hydraulic Engineering - Proceedings of 16th IAHR-APD Congress and 3rd Symposium of IAHR-ISHS" discusses some serious problems of sustainable development of human society related to water resources, disaster caused by flooding or draught, environment and ecology, and introduces latest research in river engineering and fluvial processes, estuarine and coastal hydraulics, hydraulic structures and hydropower hydraulics, etc. The proceedings covers new research achievements in the Asian-Pacific region in water resources, environmental ecology, river and coastal engineering, which are especially important for developing countries all over the world. This proceedings serves as a reference for researchers in the field of water resources, water quality, water pollution and water ecology. Changkuan Zhang and Hongwu Tang both are professors at Hohai University, China.
Incomplete Categorical Data Design
Respondents to survey questions involving sensitive information, such as sexual behavior, illegal drug usage, tax evasion, and income, may refuse to answer the questions or provide untruthful answers to protect their privacy. This creates a challenge in drawing valid inferences from potentially inaccurate data. Addressing this difficulty, non-rando
GB 6675.1-2014 Translated English of Chinese Standard. GB6675.1-2014
This Part of GB 6675 specifies the basic safety requirements and their implementation and supervision, legal responsibilities, etc. for toys. The basic safety requirements include the mechanical and physical properties, flammability properties, chemical properties, electrical properties, hygienic requirements, radiance properties and labelling requirements. This Part applies to toys intended for use by children under 14 years of age. It also applies to products intended for use by children under 14 years of age which are not specially designed for play but have the function for play.
