论文标题
二进制bruijn流程
Binary De Bruijn Processes
论文作者
论文摘要
二进制时间序列数据在许多应用程序中非常普遍，通常是通过单个成功概率的Bernoulli过程独立建模的。但是，成功的概率可能取决于过去事件的结果成功。这里提出的是一种新颖的方法，用于建模二进制时间序列数据，称为二进制de bruijn过程，该数据考虑了时间相关性。该结构源自de bruijn图 - 有向图，其中给定一组符号，v和一个“单词”长度，m，该图的节点由长度为m的所有可能序列组成。 de bruijn图等效于MTH Order Markov链，其中“单词”长度控制每个单个状态取决于的状态数量。这增加了更广泛的区域的相关性。为了量化从de bruijn过程产生的序列的聚集，观察到字母的运行长度与运行长度属性一起观察到。还提出了推理以及两个申请示例：降水数据以及牛津和剑桥船比赛。
Binary time series data are very common in many applications, and are typically modelled independently via a Bernoulli process with a single probability of success. However, the probability of a success can be dependent on the outcome successes of past events. Presented here is a novel approach for modelling binary time series data called a binary de Bruijn process which takes into account temporal correlation. The structure is derived from de Bruijn Graphs - a directed graph, where given a set of symbols, V, and a 'word' length, m, the nodes of the graph consist of all possible sequences of V of length m. De Bruijn Graphs are equivalent to mth order Markov chains, where the 'word' length controls the number of states that each individual state is dependent on. This increases correlation over a wider area. To quantify how clustered a sequence generated from a de Bruijn process is, the run lengths of letters are observed along with run length properties. Inference is also presented along with two application examples: precipitation data and the Oxford and Cambridge boat race.
