(Neal and Hinton, 1998) recast maximum likelihood estimation of any given latent variable model as the minimization of a free energy functional $F$, and the EM algorithm as coordinate descent applied to $F$. Here, we explore alternative ways to optimize the functional. In particular, we identify various gradient flows associated with $F$ and show that their limits coincide with $F$'s stationary points. By discretizing the flows, we obtain practical particle-based algorithms for maximum likelihood estimation in broad classes of latent variable models. The novel algorithms scale to high-dimensional settings and perform well in numerical experiments.

翻译：(Neal and Hinton, 1998年) 对任何特定的潜在变量模型进行最大可能性估算,将一个自由能源功能值美元作为最小值,而EM算法作为协调下降法适用于美元。在这里,我们探索优化功能的替代方法。特别是,我们确定与美元相关的各种梯度流,并表明其极限与美元固定点相吻合。通过将流量分解,我们获得了实用的基于粒子的算法,以便在广泛的潜在变量模型类别中进行最大可能性估算。新的算法规模适用于高维设置,在数字实验中表现良好。