Cyclical Kernel Adaptive Metropolis
Machine Learning
2022-07-01 v2 Machine Learning
Abstract
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.
Cite
@article{arxiv.2206.14421,
title = {Cyclical Kernel Adaptive Metropolis},
author = {Jianan Canal Li and Yimeng Zeng and Wentao Guo},
journal= {arXiv preprint arXiv:2206.14421},
year = {2022}
}