论文标题
概率模型中参数估计的新方法：最小概率流
A new method for parameter estimation in probabilistic models: Minimum probability flow
论文作者
论文摘要
由于分区函数的一般棘手性，将概率模型拟合到数据通常很困难。我们提出了一种新的参数拟合方法，最小概率流（MPF），该方法适用于任何参数模型。我们在两种情况下使用MPF证明了参数估计：连续状态空间模型和Ising自旋玻璃。在后一种情况下，它在收敛时间至少要比当前技术的表现优于当前技术，而回收的耦合参数中的误差较低。
Fitting probabilistic models to data is often difficult, due to the general intractability of the partition function. We propose a new parameter fitting method, Minimum Probability Flow (MPF), which is applicable to any parametric model. We demonstrate parameter estimation using MPF in two cases: a continuous state space model, and an Ising spin glass. In the latter case it outperforms current techniques by at least an order of magnitude in convergence time with lower error in the recovered coupling parameters.
