论文标题
潜在变量模型的最大似然训练的粒子算法
Particle algorithms for maximum likelihood training of latent variable models
论文作者
论文摘要
（Neal和Hinton，1998）任何给定潜在变量模型的重铸最大似然估计是最小化的自由能函数$ f $，而EM算法作为坐标下降，将其应用于$ f $。在这里，我们探讨了优化功能的替代方法。特别是，我们确定了与$ f $相关的各种梯度流，并表明它们的限制与$ f $的固定点一致。通过离散流量，我们获得了基于粒子的实用算法，以在广泛类别的潜在变量模型中获得最大似然估计。新型算法刻度到高维设置，并在数值实验中表现良好。
(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.
