Combining, Modelling and Analyzing Imprecision, Randomness and Dependence da Ansari, Jonathan (Editor)/ Fuchs, Sebastian (Editor)/ Trutschnig, Wolfgang (Editor)/ Lubiano , Mar�a Asunci�n (Editor)/ Gil, Mar�a �ngeles (Editor): Brand New Paperback (2024)

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Titolo: Combining, Modelling and Analyzing Imprecision, Randomness and Dependence
Editore: Springer-Nature New York Inc
Data di pubblicazione: 2024
Lingua: Inglese
ISBN 10: 3031659929
ISBN 13: 9783031659928
Rilegatura: Paperback
Condizione: Brand New

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Riassunto

This volume contains more than 65 peer-reviewed papers corresponding to presentations at the 11th Conference on Soft Methods in Probability and Statistics (SMPS) held in Salzburg, Austria, in September 2024. It covers recent advances in the field of probability, statistics, and data science, with a particular focus on dealing with dependence, imprecision and incomplete information. Reflecting the fact that data science continues to evolve, this book serves as a bridge between different groups of experts, including statisticians, mathematicians, computer scientists, and engineers, and encourages interdisciplinary research. The selected contributions cover a wide range of topics such as imprecise probabilities, random sets, belief functions, possibility theory, and dependence modeling. Readers will find discussions on clustering, depth concepts, dimensionality reduction, and robustness, reflecting the conference's commitment to addressing real-world challenges through innovative methods.

Dalla quarta di copertina

This volume contains more than 65 peer-reviewed papers corresponding to presentations at the 11th Conference on Soft Methods in Probability and Statistics (SMPS) held in Salzburg, Austria, in September 2024. It covers recent advances in the field of probability, statistics, and data science, with a particular focus on dealing with dependence, imprecision and incomplete information. Reflecting the fact that data science continues to evolve, this book serves as a bridge between different groups of experts, including statisticians, mathematicians, computer scientists, and engineers, and encourages interdisciplinary research. The selected contributions cover a wide range of topics such as imprecise probabilities, random sets, belief functions, possibility theory, and dependence modeling. Readers will find discussions on clustering, depth concepts, dimensionality reduction, and robustness, reflecting the conference's commitment to addressing real-world challenges through innovative methods.

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