Structured Statistical Models of Inductive Reasoning

Autor/inn/en	Kemp, Charles; Tenenbaum, Joshua B.
Titel	Structured Statistical Models of Inductive Reasoning
Quelle	In: Psychological Review, 116 (2009) 1, S.20-58 (39 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-295X
DOI	10.1037/a0014282
Schlagwörter	Logical Thinking; Inferences; Statistical Inference; Models; Bayesian Statistics; Prior Learning + Suchen Sie Ihr Suchwort? Inference; Inferenz; Inferential statistics; Schließende Statistik; Analogiemodell; Vorkenntnisse
Abstract	Everyday inductive inferences are often guided by rich background knowledge. Formal models of induction should aim to incorporate this knowledge and should explain how different kinds of knowledge lead to the distinctive patterns of reasoning found in different inductive contexts. This article presents a Bayesian framework that attempts to meet both goals and describe 4 applications of the framework: a taxonomic model, a spatial model, a threshold model, and a causal model. Each model makes probabilistic inferences about the extensions of novel properties, but the priors for the 4 models are defined over different kinds of structures that capture different relationships between the categories in a domain. The framework therefore shows how statistical inference can operate over structured background knowledge, and the authors argue that this interaction between structure and statistics is critical for explaining the power and flexibility of human reasoning. (Contains 19 figures, 4 tables and 8 footnotes.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10