论文标题
确定性的Langevin Monte Carlo具有标准化贝叶斯推断的流量
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference
论文作者
论文摘要
我们建议使用昂贵的可能性的通用贝叶斯推论算法，用确定性密度梯度项代替Langevin方程中的随机项。使用归一流的流（NF）从当前粒子位置评估粒子密度，该流量是可区分的，并且具有良好的泛化特性。我们利用NF预处理和基于NF的大都市 - 杂货店更新，以更快地收敛。我们在各种示例中表明，该方法与最先进的采样方法具有竞争力。
We propose a general purpose Bayesian inference algorithm for expensive likelihoods, replacing the stochastic term in the Langevin equation with a deterministic density gradient term. The particle density is evaluated from the current particle positions using a Normalizing Flow (NF), which is differentiable and has good generalization properties in high dimensions. We take advantage of NF preconditioning and NF based Metropolis-Hastings updates for a faster convergence. We show on various examples that the method is competitive against state of the art sampling methods.
