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Bayesian Robustness
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Making Sense of Large Social Media Corpora
This open access book offers a comprehensive overview of available techniques and approaches to explore large social media corpora, using as an illustrative case study the Coronavirus Twitter corpus. First, the author describes in detail a number of methods, strategies, and tools that can be used to access, manage, and explore large Twitter/X corpora, including both user-friendly applications and more advanced methods that involve the use of data management skills and custom programming scripts. He goes on to show how these tools and methods are applied to explore one of the largest Twitter datasets on the COVID-19 pandemic publicly released, covering the two years when the pandemic had the strongest impact on society. Specifically, keyword extraction, topic modelling, sentiment analysis, and hashtag analysis methods are described, contrasted, and applied to extract information from the Coronavirus Twitter Corpus. The book will be of interest to students and researchers in fields that make use of big data to address societal and linguistic concerns, including corpus linguistics, sociology, psychology, and economics.
Advances in Mathematical Modeling for Reliability
Advances in Mathematical Modeling for Reliability discusses fundamental issues on mathematical modeling in reliability theory and its applications. Beginning with an extensive discussion of graphical modeling and Bayesian networks, the focus shifts towards repairable systems: a discussion about how sensitive availability calculations parameter choices, and emulators provide the potential to perform such calculations on complicated systems to a fair degree of accuracy and in a computationally efficient manner. Another issue that is addressed is how competing risks arise in reliability and maintenance analysis through the ways in which data is censored. Mixture failure rate modeling is also a point of discussion, as well as the signature of systems, where the properties of the system through the signature from the probability distributions on the lifetime of the components are distinguished. The last three topics of discussion are relations among aging and stochastic dependence, theoretical advances in modeling, inference and computation, and recent advances in recurrent event modeling and inference.
The Oxford Handbook of Applied Bayesian Analysis
Bayesian analysis has developed rapidly in applications in the last two decades and research in Bayesian methods remains dynamic and fast-growing. Dramatic advances in modelling concepts and computational technologies now enable routine application of Bayesian analysis using increasingly realistic stochastic models, and this drives the adoption of Bayesian approaches in many areas of science, technology, commerce, and industry. This Handbook explores contemporary Bayesian analysis across a variety of application areas. Chapters written by leading exponents of applied Bayesian analysis showcase the scientific ease and natural application of Bayesian modelling, and present solutions to real, engaging, societally important and demanding problems. The chapters are grouped into five general areas: Biomedical & Health Sciences; Industry, Economics & Finance; Environment & Ecology; Policy, Political & Social Sciences; and Natural & Engineering Sciences, and Appendix material in each touches on key concepts, models, and techniques of the chapter that are also of broader pedagogic and applied interest.
The Contribution of Young Researchers to Bayesian Statistics
The first Bayesian Young Statisticians Meeting, BAYSM 2013, has provided a unique opportunity for young researchers, M.S. students, Ph.D. students, and post-docs dealing with Bayesian statistics to connect with the Bayesian community at large, exchange ideas, and network with scholars working in their field. The Workshop, which took place June 5th and 6th 2013 at CNR-IMATI, Milan, has promoted further research in all the fields where Bayesian statistics may be employed under the guidance of renowned plenary lecturers and senior discussants. A selection of the contributions to the meeting and the summary of one of the plenary lectures compose this volume.
Image Analysis and Processing - ICIAP 2025 Workshops
This two-volume set LNCS 16169–16170 constitutes the refereed proceedings of the 23rd International Conference on Image Analysis and Processing, ICIAP 2025 Workshops, held in Rome, Italy, during September 15–19, 2025. The 105 papers included in these proceedings were carefully reviewed and selected from 348 submissions. The papers are organized in the following topical sections: Volume I: 4th Automatic Affect Analysis and Synthesis Workshop (3AS 2025); International Workshop on Advances in Drone Vision (ADV 2025); 3rd International Workshop on Artificial Intelligence and Radiomics in Computer-Aided Diagnosis (AIRCAD 2025); 2nd International Workshop on Computer Vision for Environment Mon...
Bayesian Methods
An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social ScientistsNow that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details of th
Sensitivity Analysis
Sensitivity analysis is used to ascertain how a given model output depends upon the input parameters. This is an important method for checking the quality of a given model, as well as a powerful tool for checking the robustness and reliability of its analysis. The topic is acknowledged as essential for good modelling practice, and is an implicit part of any modelling field. · Offers an accessible introduction to sensitivity analysis · Covers all the latest research · Illustrates concepts with numerous examples, applications and case studies · Includes contributions form the leading researchers active in developing strategies for sensitivity analysis The principles of sensitivity analysis...
Bayesian Statistics 8
The Valencia International Meetings on Bayesian Statistics provide the main forum for researchers in Bayesian Statistics. This eighth proceedings offers the reader a wide perspective of the developments in Bayesian statistics over the last four years.
