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Hierarchical Bayesian models as formal models of causal reasoning
Argument and Computation 4 (1):36 - 45 (2013)
Abstract
(2013). Hierarchical Bayesian models as formal models of causal reasoning. Argument & Computation: Vol. 4, Formal Models of Reasoning in Cognitive Psychology, pp. 36-45. doi: 10.1080/19462166.2012.700321Author's Profile
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Citations of this work
Successful structure learning from observational data.Anselm Rothe, Ben Deverett, Ralf Mayrhofer & Charles Kemp - 2018 - Cognition 179 (C):266-297.
References found in this work
The weirdest people in the world?Joseph Henrich, Steven J. Heine & Ara Norenzayan - 2010 - Behavioral and Brain Sciences 33 (2-3):61-83.
A Theory of Causal Learning in Children: Causal Maps and Bayes Nets.Alison Gopnik, Clark Glymour, Laura Schulz, Tamar Kushnir & David Danks - 2004 - Psychological Review 111 (1):3-32.
Causality: Models, reasoning and inference.Christopher Hitchcock - 2001 - Philosophical Review 110 (4):639-641.
The Probabilistic Mind: Prospects for Bayesian Cognitive Science.Nick Chater & Mike Oaksford (eds.) - 2008 - Oxford University Press.
Inferring causal networks from observations and interventions.Mark Steyvers, Joshua B. Tenenbaum, Eric-Jan Wagenmakers & Ben Blum - 2003 - Cognitive Science 27 (3):453-489.
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