nips nips2002 nips2002-198 nips2002-198-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Joshua B. Tenenbaum, Thomas L. Griffiths
Abstract: People routinely make sophisticated causal inferences unconsciously, effortlessly, and from very little data – often from just one or a few observations. We argue that these inferences can be explained as Bayesian computations over a hypothesis space of causal graphical models, shaped by strong top-down prior knowledge in the form of intuitive theories. We present two case studies of our approach, including quantitative models of human causal judgments and brief comparisons with traditional bottom-up models of inference.
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