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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021
摘要
Even simply-defined, finite-state generators produce 隨機 processes that require tracking an uncountable infinity of probabilistic features for optimal prediction. For processes generated by hidden 馬可夫 chains the consequences are dramatic. Their predictive models are generically infinite-state. And, until recently, one could determine neither their intrinsic randomness nor structural complexity. The prequel, though, introduced methods to accurately calculate the Shannon 熵 rate (randomness) and to constructively determine their minimal (though, infinite) set of predictive features. Leveraging this, we address the complementary challenge of determining how structured hidden 馬可夫 processes are by calculating their 統計 complexity dimension—the 資訊 dimension of the minimal set of predictive features
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