Sequential Monte Carlo squared (SMC$^2$; Chopin et al., 2012) methods can be used to sample from the exact posterior distribution of intractable likelihood state space models. These methods are the SMC analogue to particle Markov chain Monte Carlo (MCMC; Andrieu et al., 2010) and rely on particle MCMC kernels to mutate the particles at each iteration. Two options for the particle MCMC kernels are particle marginal Metropolis-Hastings (PMMH) and particle Gibbs (PG). We introduce a method to adaptively select the particle MCMC kernel at each iteration of SMC$^2$, with a particular focus on switching between a PMMH and PG kernel. The resulting method can significantly improve the efficiency of SMC$^2$ compared to using a fixed particle MCMC kernel throughout the algorithm. Code for our methods is available at https://github.com/imkebotha/kernel_switching_smc2.

翻译：序列蒙特卡洛平方（SMC$^2$；Chopin等，2012）方法可用于从难以处理似然的状态空间模型的精确后验分布中采样。这些方法是粒子马尔可夫链蒙特卡洛（MCMC；Andrieu等，2010）的SMC模拟，并依赖粒子MCMC核在每次迭代中突变粒子。粒子MCMC核的两个选项是粒子边缘Metropolis-Hastings（PMMH）和粒子Gibbs（PG）。我们提出了一种在SMC$^2$每次迭代中自适应选择粒子MCMC核的方法，特别关注在PMMH和PG核之间切换。与在整个算法中使用固定粒子MCMC核相比，由此产生的方法能显著提高SMC$^2$的效率。我们方法的代码可在https://github.com/imkebotha/kernel_switching_smc2 获取。