论文标题
PEXM：用于涉及分段指数分布的应用的JAGS模块
pexm: a JAGS module for applications involving the piecewise exponential distribution
论文作者
论文摘要
在这项研究中，我们提出了一个新的模块，该模块为对编程语言感兴趣的用户构建，类似于错误，以适合基于分段指数（PE）分布的贝叶斯模型。该模块是开源程序JAGS的扩展，可以通过该计划应用Gibbs采样器，而无需衍生完整条件和随后实施策略以从未知分布中绘制样本。 PE分布广泛用于生存分析和可靠性领域。目前，只能通过JAG通过方法实现，以间接根据Poisson或Bernoulli概率指定可能性。我们的模块提供了更直接的实现，因此对于旨在花费更多时间探索贝叶斯推论的结果而不是实施马尔可夫链蒙特卡洛（MCMC）算法的研究人员更具吸引力。对于那些有兴趣扩展JAG的人，这项工作可以看作是一个教程，其中包括在其他材料中未经良好调查或组织的重要信息。在这里，我们描述了如何利用R和JAG之间的接口的模块。开发了一项简短的仿真研究，以确保模块的行为良好，并且涉及两个PE模型的真实例证展示了可以在实践中使用该模块的环境。
In this study, we present a new module built for users interested in a programming language similar to BUGS to fit a Bayesian model based on the piecewise exponential (PE) distribution. The module is an extension to the open-source program JAGS by which a Gibbs sampler can be applied without requiring the derivation of complete conditionals and the subsequent implementation of strategies to draw samples from unknown distributions. The PE distribution is widely used in the fields of survival analysis and reliability. Currently, it can only be implemented in JAGS through methods to indirectly specify the likelihood based on the Poisson or Bernoulli probabilities. Our module provides a more straightforward implementation and is thus more attractive to the researchers aiming to spend more time exploring the results from the Bayesian inference rather than implementing the Markov Chain Monte Carlo (MCMC) algorithm. For those interested in extending JAGS, this work can be seen as a tutorial including important information not well investigated or organized in other materials. Here, we describe how to use the module taking advantage of the interface between R and JAGS. A short simulation study is developed to ensure that the module behaves well and a real illustration, involving two PE models, exhibits a context where the module can be used in practice.
