# Polynomial time guarantees for sampling based posterior inference in high-dimensional generalised linear models

@article{Altmeyer2022PolynomialTG, title={Polynomial time guarantees for sampling based posterior inference in high-dimensional generalised linear models}, author={Randolf Altmeyer}, journal={ArXiv}, year={2022}, volume={abs/2208.13296} }

The problem of computing posterior functionals in general high-dimensional statistical models with possibly non-log-concave likelihood functions is considered. Based on the proof strategy of [56], but using only local likelihood conditions and without relying on M-estimation theory, non-asymptotic statistical and computational guarantees are provided for gradient based MCMC algorithms. Given a suitable initialiser, these guarantees scale polynomially in key algorithmic quantities. The abstract…

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