相交上下文树模型的联合估计
Joint estimation of intersecting context tree models
摘要：
我们研究了两个不同的上下文树源产生的数据的模型选择问题。出于语言学问题，我们考虑的情况下，对应于两个源的概率上下文树是有限的，并共享许多他们的上下文。为了理解这两个源之间的差异，重要的是要识别哪些上下文和哪些转移概率是特定于每个源的。我们考虑一类概率的上下文树模型与三种类型的上下文：那些出现在一个，其他的，或两个源。我们使用一个BIC惩罚最大似然过程，共同估计这两个来源。我们提出了一种新的算法，有效地计算估计的上下文树。我们证明了该过程是强相容的。我们还提出了一个模拟研究，显示了我们的程序的实际优势，在每个数据集上单独工作的程序。
英文摘要：
We study a problem of model selection for data produced by two different context tree sources. Motivated by linguistic questions, we consider the case where the probabilistic context trees corresponding to the two sources are finite and share many of their contexts. In order to understand the differences between the two sources, it is important to identify which contexts and which transition probabilities are specific to each source. We consider a class of probabilistic context tree models with three types of contexts: those which appear in one, the other, or both sources. We use a BIC penalized maximum likelihood procedure that jointly estimates the two sources. We propose a new algorithm which efficiently computes the estimated context trees. We prove that the procedure is strongly consistent. We also present a simulation study showing the practical advantage of our procedure over a procedure that works separately on each dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
