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Learning to Learn Causal Models
2010
Cognitive Science
Learning to understand a single causal system can be an achievement, but humans must learn about multiple causal systems over the course of a lifetime. We present a hierarchical Bayesian framework that helps to explain how learning about several causal systems can accelerate learning about systems that are subsequently encountered. Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal
doi:10.1111/j.1551-6709.2010.01128.x
pmid:21564248
fatcat:fbnmbz5s7nhelid3m4vcvc56u4