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原文連結
論文資訊
- 類型：已發表論文
- 日期：2010
摘要
We consider the value of structured priors in the analysis of data sampled from complex adaptive systems. We propose that adaptive dynamics entails basic constraints (memory, 資訊 processing) and features (optimization and 演化ary history) that serve to significantly narrow search spaces and candidate parameter values. We suggest that the property of "adaptive self-awareness", when applicable, further constrains model selection, such that predictive 統計 models converge on a systems own internal representation of regularities. Principled model building should therefore begin by identifying a hierarchy of increasingly constrained models based on the adaptive properties of the study system.
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