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arXiv · 2206.14421

Cyclical Kernel Adaptive Metropolis

Jianan Canal Li·Yimeng Zeng·Wentao Guo
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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.

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Jianan Canal Li, Yimeng Zeng, Wentao Guo. 2022-06-29. Cyclical Kernel Adaptive Metropolis. https://arxiv.org/abs/2206.14421

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