Ensemble Kalman inversion represents a powerful technique for inference in statistical models with likelihoods of the form $y \mid x \sim \mathcal{N}(y \mid \mathcal{H}(x),\mathrm{R})$ where the forward operator $\mathcal{H}$ and covariance $\mathrm{R}$ are known. In this article, we generalise ensemble Kalman inversion to models with general likelihoods, $y \mid x \sim p(y \mid x)$ where the likelihood can be sampled from, but its density not necessarily evaluated. We examine the ensemble Kalman performance for both optimisation and uncertainty quantification against fully adaptive approximate Bayesian computation techniques.

翻译：在已知远端操作员$\mathcal{H}(x),\mathrm{R}美元和共差$\mathrm{R}的情况下,Kalman的反转是一种强有力的统计模型推论技术。在本篇文章中,我们笼统地将共和Kalman的反射转换为具有一般可能性的模型,即$y $y mid x\sim p(y\midx)$,其中有可能取样,但其密度不一定得到评估。我们根据完全适应性近似贝叶斯计算技术,检查共性卡尔曼的性能,以优化和不确定性量化。