Data Science: Measuring Uncertainties II
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 23839
Special Issue Editors
Interests: Bayesian statistics; controversies and paradoxes in probability and statistics; Bayesian reliability; Bayesian analysis of discrete data (BADD); applied statistics
Special Issues, Collections and Topics in MDPI journals
Interests: Bayesian inference; data science; foundations of statistics; model selection; reliability and survival analysis; significance test
Special Issues, Collections and Topics in MDPI journals
Interests: data analysis; statistical analysis; statistical modeling; applied statistics; R statistical package
Special Issue Information
Dear Colleagues,
The demand for data analysis is increasing day by day, and this is reflected in a large number of jobs and the high number of published articles. New solutions to the problems seem to be reproducing at a massive rate. A new era is coming! The dazzle is so great that many of us do not bother to check the suitability of the solutions for the problems that they are intended to solve. Current and future challenges require greater care in the creation of new solutions satisfying the rationality of each type of problem. Labels such as big data, data science, machine learning, statistical learning, and artificial intelligence are demanding more sophistication in the fundamentals and in the way that they are being applied.
This Special Issue is dedicated to solutions for and discussions of measuring uncertainties in data analysis problems. For example, considering the large amount of data related to an IoT (internet of things) problem, or even considering the small sample size of a biological study with huge dimensions, one must show how to properly understand the data, how to develop the best process of analysis and, finally, to illustrate how to apply the solutions that were obtained theoretically. We seek to respond to these challenges and publish papers that consider the reasons for a solution and how to apply them. Papers can cover existing methodologies by elucidating questions related to the reasons for their selection and their uses.
We are open to innovative solutions and theoretical works that justify the use of a method and to applied works that describe a good implementation of a theoretical method.
Prof. Dr. Carlos Alberto De Bragança Pereira
Prof. Dr. Adriano Polpo
Dr. Agatha Rodrigues
Dr. Débora Corrêa
Guest Editors
Manuscript Submission Information
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Related Special Issue
- Data Science: Measuring Uncertainties in Entropy (13 articles)