In this article, we present a variational approach to Gaussian and mixture-of-Gaussians assumed filtering. Our method relies on an approximation stemming from the gradient-flow representations of a Kullback--Leibler discrepancy minimization. We outline the general method and show its competitiveness in parameter estimation and posterior representation for two models for which Gaussian approximations typically fail: a multiplicative noise and a multi-modal model.

翻译：本文提出一种针对高斯及高斯混合假设滤波的变分方法。该方法基于Kullback-Leibler散度最小化的梯度流表示导出近似框架。我们阐述了该通用方法，并通过两种典型高斯近似失效模型（乘性噪声模型与多模态模型）的参数估计及后验表征实验，验证了其竞争力。