论文标题
周期性内核自适应大都市
Cyclical Kernel Adaptive Metropolis
论文作者
论文摘要
我们提出了CKAM，周期性内核自适应大都市，该大都市结合了一个周期性的台阶方案，以控制探索和采样。我们表明，在精心设计的双峰分布中，现有的自适应大都市型算法将无法融合到真正的后验分布。我们指出，这是因为自适应采样器使用链的过去历史估算局部/全局协方差结构，这将导致自适应算法被困在局部模式下。我们证明CKAM鼓励对后验分布进行探索，并允许采样器从局部模式中逃脱，同时保持自适应方法的高性能。
We propose cKAM, cyclical Kernel Adaptive Metropolis, which incorporates a cyclical stepsize scheme to allow control for exploration and sampling. We show that on a crafted bimodal distribution, existing Adaptive Metropolis type algorithms would fail to converge to the true posterior distribution. We point out that this is because adaptive samplers estimates the local/global covariance structure using past history of the chain, which will lead to adaptive algorithms be trapped in a local mode. We demonstrate that cKAM encourages exploration of the posterior distribution and allows the sampler to escape from a local mode, while maintaining the high performance of adaptive methods.
