Название: Building Bridges between Soft and Statistical Methodologies for Data Science
Автор: Luis A. García-Escudero, Alfonso Gordaliza, Agustín Mayo
Издательство: Springer
Год: 2023
Страниц: 421
Язык: английский
Формат: pdf (true)
Размер: 12.8 MB

Nowadays, data analysis is becoming an appealing topic due to the emergence of new data types, dimensions, and sources. This motivates the development of probabilistic/statistical approaches and tools to cope with these data. Different communities of experts, namely statisticians, mathematicians, computer scientists, engineers, econometricians, and psychologists are more and more interested in facing this challenge. As a consequence, there is a clear need to build bridges between all these communities for Data Science.

Soft methods are designed either to address, among others, difficulties related to imprecise or other complex data, or to create/combine alternatives to deal with traditional data. Consequently, they will certainly play an important role in the near future to cope with these current challenges. Furthermore, contaminated data are ubiquitous, and therefore, robust methodologies are required when certain degree of inaccuracy and noise are present in data.

The volume contains more than fifty selected contributions that are clearly useful in establishing those important bridges between soft and statistical methodologies for Data Science. These contributions cover very different and relevant aspects such as imprecise probabilities, information theory, random sets and random fuzzy sets, belief functions, possibility theory, dependence modeling and copulas, clustering, depth concepts, dimensionality reduction of complex data and robustness.