# A unified performance analysis of likelihood-informed subspace methods

@inproceedings{Cui2021AUP, title={A unified performance analysis of likelihood-informed subspace methods}, author={Tiangang Cui and Xin Tong}, year={2021} }

The likelihood-informed subspace (LIS) method offers a viable route to reducing the dimensionality of highdimensional probability distributions arising in Bayesian inference. LIS identifies an intrinsic low-dimensional linear subspace where the target distribution differs the most from some tractable reference distribution. Such a subspace can be identified using the leading eigenvectors of a Gram matrix of the gradient of the log-likelihood function. Then, the original high-dimensional target…

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