Interacting Contour Stochastic Gradient Langevin Dynamics [article]

Wei Deng, Siqi Liang, Botao Hao, Guang Lin, Faming Liang
2022 arXiv   pre-print
We propose an interacting contour stochastic gradient Langevin dynamics (ICSGLD) sampler, an embarrassingly parallel multiple-chain contour stochastic gradient Langevin dynamics (CSGLD) sampler with efficient interactions. We show that ICSGLD can be theoretically more efficient than a single-chain CSGLD with an equivalent computational budget. We also present a novel random-field function, which facilitates the estimation of self-adapting parameters in big data and obtains free mode
more » ... . Empirically, we compare the proposed algorithm with popular benchmark methods for posterior sampling. The numerical results show a great potential of ICSGLD for large-scale uncertainty estimation tasks.
arXiv:2202.09867v1 fatcat:cppv7zq3f5efhpkyzf32btowm4