We present a two headed approach called Bayesian Integrated Coalescent Epoch PlotS (BICEPS) for efficient inference of coalescent epoch models. Firstly, we integrate out population size parameters and secondly we introduce a set of more powerful Markov chain Monte Carlo (MCMC) proposals for flexing and stretching trees. Even though population sizes are integrated out and not explicitly sampled through MCMC, we are still able to generate samples from the population size posteriors. This allows demographic reconstruction through time and estimating the timing and magnitude of population bottlenecks and full population histories. Altogether, BICEPS can be considered a more muscular version of the popular Bayesian skyline model. We demonstrate its power and correctness by a well calibrated simulation study. Furthermore, we demonstrate with an application to SARS-CoV-2 genomic data that some analyses that have trouble converging with the traditional Bayesian skyline prior and standard MCMC proposals can do well with the BICEPS approach. BICEPS is available as open source package for BEAST 2 under GPL license and has a user friendly graphical user interface.

中文翻译：

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贝叶斯系统发育推理的有效合并时代模型

我们提出了一种称为贝叶斯综合合并时代图 (BICEPS) 的双头方法，用于有效推断合并时代模型。首先，我们整合了人口规模参数，其次我们引入了一组更强大的马尔可夫链蒙特卡罗 (MCMC) 提议，用于弯曲和拉伸树。即使人口规模被整合出来并且没有通过 MCMC 明确采样，我们仍然能够从人口规模后验中生成样本。这允许通过时间进行人口重建，并估计人口瓶颈和完整人口历史的时间和规模。总而言之，BICEPS 可以被认为是流行的贝叶斯天际线模型的更强壮版本。我们通过经过良好校准的模拟研究来证明其功能和正确性。此外，我们通过对 SARS-CoV-2 基因组数据的应用证明，一些难以与传统的贝叶斯天际线先验和标准 MCMC 提议收敛的分析可以用 BICEPS 方法很好地完成。BICEPS 在 GPL 许可下作为 BEAST 2 的开源包提供，并具有用户友好的图形用户界面。