论文标题
罕见的事件ABC-SMC $^{2} $
Rare event ABC-SMC$^{2}$
论文作者
论文摘要
近似贝叶斯计算（ABC）是一个良好的蒙特卡洛方法家族，用于在数据中使用``隐式''模型的情况下进行近似贝叶斯推断的情况：当可以模拟数据模型时，但不能轻易评估这些可能性的可能性。标准ABC方法的基本属性是，通过数据尺寸，实现给定准确性量表所需的蒙特卡洛点数数量。 Prangle等。 （2018）提出了一种马尔可夫链蒙特卡洛（MCMC）方法，该方法使用罕见的事件顺序蒙特卡洛（SMC）方法来估计避免这种指数缩放的ABC可能性，从而允许ABC在更高的维数据上使用。本文以Prangle等人的作品为基础。 （2018）通过在SMC算法中使用罕见的事件SMC方法，而不是在MCMC算法中。新方法的结构与SMC $^{2} $（Chopin等，2013）相似，并且比MCMC方法需要更少的调整。与现有的ABC-SMC方法相比，我们在玩具示例和用于建模蛋白质相互作用网络的重复差异随机图模型上演示了新方法。
Approximate Bayesian computation (ABC) is a well-established family of Monte Carlo methods for performing approximate Bayesian inference in the case where an ``implicit'' model is used for the data: when the data model can be simulated, but the likelihood cannot easily be pointwise evaluated. A fundamental property of standard ABC approaches is that the number of Monte Carlo points required to achieve a given accuracy scales exponentially with the dimension of the data. Prangle et al. (2018) proposes a Markov chain Monte Carlo (MCMC) method that uses a rare event sequential Monte Carlo (SMC) approach to estimating the ABC likelihood that avoids this exponential scaling, and thus allows ABC to be used on higher dimensional data. This paper builds on the work of Prangle et al. (2018) by using the rare event SMC approach within an SMC algorithm, instead of within an MCMC algorithm. The new method has a similar structure to SMC$^{2}$ (Chopin et al., 2013), and requires less tuning than the MCMC approach. We demonstrate the new approach, compared to existing ABC-SMC methods, on a toy example and on a duplication-divergence random graph model used for modelling protein interaction networks.
