Combining, Modelling and Analyzing Imprecision, Randomness and Dependence
Ansari, Jonathan (EDT); Fuchs, Sebastian (EDT); Trutschnig, Wolfgang (EDT); Lubiano , María Asunción (EDT); Gil, María Ángeles (EDT)
Language: English
Published by Springer, 2024
- Softcover
- Used
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- Title
- Combining, Modelling and Analyzing Imprecision, Randomness and Dependence
- Author
- Ansari, Jonathan (EDT); Fuchs, Sebastian (EDT); Trutschnig, Wolfgang (EDT); Lubiano , María Asunción (EDT); Gil, María Ángeles (EDT)
- Publisher
- Springer
- Publication year
- 2024
- Condition
- As New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 3031659929
- ISBN 13
- 9783031659928
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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