04174nam a22005655i 4500 978-3-031-77892-6 DE-He213 20251030114928.0 cr nn 008mamaa 250122s2024 sz | s |||| 0|eng d 9783031778926 978-3-031-77892-6 10.1007/978-3-031-77892-6 doi Q174-175.3 B67 PDA bicssc SCI075000 bisacsh PDA thema Perspectives on Logics for Data-driven Reasoning [electronic resource] / edited by Hykel Hosni, Jürgen Landes. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XIII, 207 p. 13 illus., 6 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Logic, Argumentation & Reasoning, Interdisciplinary Perspectives from the Humanities and Social Sciences, 2214-9139 ; 35 Chapter 1. A note on logic and the methodology of data-driven science (Hosni and Landes) -- Chapter 2. Pure Inductive Logic (Vencovska) -- Chapter 3. Where do we stand on maximal entropy? (Williamson) -- Chapter 4. Probability logic and statistical relational artificial intelligence (Weitkamper) -- Chapter 5. An Overview of the Generalization Problem (Facciuto) -- Chapter 6. The Logic of DNA Identification (Zabell) -- Chapter 7. Reasoning With and About Bias (Manganini and Primiero) -- Chapter 8. Knowledge Representation, Scientific Argumentation and Non-monotonic Logic (Landes et al) -- Chapter 9. Reasoning with Data in the framework of a Quantum Approach to Machine Learning (Chiara et al). This book calls for a rethinking of logic as the core methodological tool for scientific reasoning in the context of a steadily increasing emphasis on data-centered science. To do so it provides a state-of-the-art presentation of the role logic can have in making the most of the current opportunities while making explicit the key challenges opened up by the data-driven age of scientific research. Particular attention is given to the following four core fields and applications: Reasoning with correlations (medical, life-science applications); logics for statistical inference (machine learning, and societal applications thereof); reasoning with evidence (defining good evidence); causal reasoning (forensic reasoning). The book collects contributions from key logicians, methodologists and scientists. This multidisciplinary perspective benefits both scientists and logicians interested in data-driven science. Scientists are introduced to logics that go beyond classical and thus are applicable to reasoning with data; Logicians have a change to focus on the potential applications of their methods and techniques to pressing scientific problems. This book is, therefore, of interest to scientists and logicians working on data-centered science. Science Philosophy. 63307 Philosophy. 66189 Statistics . 65982 Philosophy of Science. 68560 Philosophy. 66189 Applied Statistics. 65986 Hosni, Hykel. editor. edt http://id.loc.gov/vocabulary/relators/edt 90377 Landes, Jürgen. editor. edt http://id.loc.gov/vocabulary/relators/edt 90378 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031778919 Printed edition: 9783031778933 Printed edition: 9783031778940 Logic, Argumentation & Reasoning, Interdisciplinary Perspectives from the Humanities and Social Sciences, 2214-9139 ; 35 90379 https://doi.org/10.1007/978-3-031-77892-6 ZDB-2-REP ZDB-2-SXPR 66477 66477