This paper examines methodology for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions. For this, we use sequential Monte Carlo samplers, introducing the innovation of using deterministic transformations to move particles effectively between target distributions with different dimensions. This approach, combined with adaptive methods, yields an extremely flexible and general algorithm for Bayesian model comparison that is suitable for use in applications where the acceptance rate in reversible jump Markov chain Monte Carlo is low. We use this approach on model comparison for mixture models, and for inferring coalescent trees sequentially, as data arrives.

中文翻译：

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顺序蒙特卡洛变换。

本文研究了在不同维数空间上的后验序列上顺序执行贝叶斯推理的方法。为此，我们使用顺序蒙特卡洛采样器，介绍了使用确定性转换在不同尺寸的目标分布之间有效移动粒子的创新。这种方法与自适应方法相结合，为贝叶斯模型比较提供了一种非常灵活且通用的算法，适用于可逆跳跃马尔可夫链蒙特卡洛中接受率较低的应用。我们将这种方法用于混合模型的模型比较，并在数据到达时顺序地推断合并树。