Change-point models are generative models in which the underlying generative parameters change at different points in time. A Bayesian approach to the problem of hazard change with unknown multiple change-points is developed using informative priors for censored survival data. For the exponential distribution, piecewise constant hazard is considered with change-point estimation. The stochastic approximation Monte Carlo algorithm is implemented for efficient calculation of the posterior distributions. The performance of the proposed estimator is checked via simulation. As a real data application, Leukemia data are analyzed by the proposed method and compared with other previous non-Bayesian method.

中文翻译：

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贝叶斯多变化点估计的危险性与指数分布的删失生存数据

变更点模型是生成模型，其中基础生成参数在不同的时间点发生变化。使用用于检查生存数据的信息先验，开发了一种贝叶斯方法，用于解决具有多个未知变化点的危险变化问题。对于指数分布，通过变化点估计来考虑分段恒定风险。为了有效地计算后验分布，实现了随机近似蒙特卡洛算法。通过仿真检查提出的估计器的性能。作为一种实际的数据应用，通过所提出的方法对白血病数据进行分析，并将其与其他先前的非贝叶斯方法进行比较。