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 and unbiased convergence. We show on various examples that the method is competitive against state of the art sampling methods.

翻译：我们提出了一个用于昂贵可能性的通用贝叶斯推论算法,用确定性密度梯度术语取代朗埃文方程式中的随机学术语。粒子密度根据目前的粒子位置使用一种可区分且具有高维特征的普通化流程(NF)进行评估。我们利用NF先决条件和以NF为基础的大都会-Hastings更新,以便更快和不偏不倚地趋同。我们用各种例子显示,该方法与最先进的取样方法相比具有竞争力。