论文标题
建模相关的Bernoulli数据第二部分：推理
Modelling Correlated Bernoulli Data Part II: Inference
论文作者
论文摘要
二进制数据在许多应用中非常普遍，但是通常以独立和相同分布的数据进行建模。在许多现实世界中，通常不是这种情况，成功的概率可能取决于过去事件的结果成功。 De Bruijn过程（DBP）是在Kimpton等人中引入的。 [2022]。这是一个相关的Bernoulli过程，可用于模拟具有已知相关性的二进制数据。相关结构通过使用de bruijn图，从而扩展了马尔可夫链。考虑到DBP和观察到的二元数据序列，我们提出了一种使用贝叶斯因子的推理方法。结果应用于牛津和剑桥年度船比赛。
Binary data are highly common in many applications, however it is usually modelled with the assumption that the data are independently and identically distributed. This is typically not the case in many real-world examples and such the probability of a success can be dependent on the outcome successes of past events. The de Bruijn process (DBP) was introduced in Kimpton et al. [2022]. This is a correlated Bernoulli process which can be used to model binary data with known correlation. The correlation structures are included through the use of de Bruijn graphs, giving an extension to Markov chains. Given the DBP and an observed sequence of binary data, we present a method of inference using Bayes' factors. Results are applied to the Oxford and Cambridge annual boat race.
