03969nam a22005175i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001500172050000800187072001600195072002300211072001500234245011900249250001800368264006900386300006500455336002600520337002600546338003600572347002400608490012700632505070500759520125701464650003202721650002302753650002502776650003402801650002302835650003102858700008202889700008602971710003403057773002603091776003603117776003603153776003603189830013403225856004603359912001403405912001503419999001703434978-3-031-77892-6DE-He21320251030114928.0cr nn 008mamaa250122s2024    sz |    s    |||| 0|eng d  a97830317789269978-3-031-77892-67 a10.1007/978-3-031-77892-62doi 4aQ174-175.3 4aB67 7aPDA2bicssc 7aSCI0750002bisacsh 7aPDA2thema10aPerspectives on Logics for Data-driven Reasoningh[electronic resource] /cedited by Hykel Hosni, Jürgen Landes.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXIII, 207 p. 13 illus., 6 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aLogic, Argumentation & Reasoning, Interdisciplinary Perspectives from the Humanities and Social Sciences,x2214-9139 ;v350 aChapter 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).  aThis 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. 0aSciencexPhilosophy.963307 0aPhilosophy.966189 0aStatistics .96598214aPhilosophy of Science.96856024aPhilosophy.96618924aApplied Statistics.9659861 aHosni, Hykel.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9903771 aLandes, Jürgen.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt9903782 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303177891908iPrinted edition:z978303177893308iPrinted edition:z9783031778940 0aLogic, Argumentation & Reasoning, Interdisciplinary Perspectives from the Humanities and Social Sciences,x2214-9139 ;v3599037940uhttps://doi.org/10.1007/978-3-031-77892-6  aZDB-2-REP  aZDB-2-SXPR  c66477d66477