BMRMM:贝叶斯马尔可夫(更新)混合模型 R 软件包

Yutong Wu, Abhra Sarkar
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引用次数: 0

摘要

我们介绍了 BMRMM 软件包,它实现了一类马尔可夫更新混合模型的贝叶斯推理,可以描述序列集合的随机动态,每个序列由分类状态的备选实例和相关的连续持续时间组成,同时受到一系列外生因素和 "随机 "个体的影响。默认设置可灵活地使用狄利克特分布混合物对状态转换概率建模,使用伽马核混合物对持续时间建模,同时还允许对两者进行变量选择。使用更简单的马尔可夫混合模型对此类数据建模也是一种选择,要么完全忽略持续时间,要么用用户指定单位离散的额外类别实例来代替持续时间。当可能无法获得持续时间数据时,该选项也很有用。我们用两个数据集来演示软件包的实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
BMRMM: An R Package for Bayesian Markov (Renewal) Mixed Models
We introduce the BMRMM package implementing Bayesian inference for a class of Markov renewal mixed models which can characterize the stochastic dynamics of a collection of sequences, each comprising alternative instances of categorical states and associated continuous duration times, while being influenced by a set of exogenous factors as well as a 'random' individual. The default setting flexibly models the state transition probabilities using mixtures of Dirichlet distributions and the duration times using mixtures of gamma kernels while also allowing variable selection for both. Modeling such data using simpler Markov mixed models also remains an option, either by ignoring the duration times altogether or by replacing them with instances of an additional category obtained by discretizing them by a user-specified unit. The option is also useful when data on duration times may not be available in the first place. We demonstrate the package's utility using two data sets.
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