SeqROCTM：上下文树模型驱动的随机对象序列分析的Matlab工具箱
SeqROCTM: A Matlab toolbox for the analysis of Sequence of Random Objects driven by Context Tree Models
摘要：
在几个研究问题中，我们处理输入的概率序列（例如，刺激序列），代理从该刺激序列生成相应的响应序列，并且感兴趣的是对它们之间的关系进行建模。一类新的随机过程，即\textit{sequences of random objects driven by context tree models}，已被引入到听觉统计学习的上下文中的这种关系建模。本文介绍了一个实现这类随机过程的免费Matlab工具箱（SeqROCTM）和三个模型选择过程，并给出了对这类随机过程进行推理的方法，此外，由于新的数学框架与上下文树模型的密切关系，该工具箱还实现了几个已有的上下文树模型的模型选择算法。
英文摘要：
In several research problems we deal with probabilistic sequences of inputs (e.g., sequence of stimuli) from which an agent generates a corresponding sequence of responses and it is of interest to model the relation between them. A new class of stochastic processes, namely \textit{sequences of random objects driven by context tree models}, has been introduced to model such relation in the context of auditory statistical learning. This paper introduces a freely available Matlab toolbox (SeqROCTM) that implements this new class of stochastic processes and three model selection procedures to make inference on it. Besides, due to the close relation of the new mathematical framework with context tree models, the toolbox also implements several existing model selection algorithms for context tree models.
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。
