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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 introduce an approach to inferring the causal architecture of 隨機 dynamical systems that extends rate-distortion theory to use causal shielding-a natural principle of learning. We study two distinct cases of causal inference: optimal causal filtering and optimal causal estimation. Filtering corresponds to the ideal case in which the probability distribution of measurement sequences is known, giving a principled method to approximate a system's causal structure at a desired level of representation. We show that in the limit in which a model-complexity constraint is relaxed, filtering finds the exact causal architecture of a 隨機 dynamical system, known as the causal-state partition. From this, one can estimate the amount of historical 資訊 the process stores. More generally, causal filtering
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