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原文連結
論文資訊
- 類型：已發表論文
- 日期：2014-04-10
摘要
We introduce a 貝氏 approach to discovering patterns in structurally complex processes. The proposed method of 貝氏 structural inference (BSI) relies on a set of candidate unifilar hidden 馬可夫 model (uHMM) topologies for inference of process structure from a data series. We employ a recently developed exact enumeration of topological is an element of-machines. (A sequel then removes the topological restriction.) This subset of the uHMM topologies has the added benefit that inferred models are guaranteed to be is an element of-machines, irrespective of estimated transition probabilities. Properties of is an element of-machines and uHMMs allow for the derivation of analytic expressions for estimating transition probabilities, inferring start states, and comparing the posterior probability of cand
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