论文标题
数据驱动的参数估计
Data-Driven Parameter Estimation
论文作者
论文摘要
最佳参数估计方法需要了解参数概率密度，该密度统计地描述了可用观测值。在这项工作中，我们在数据驱动的公式下检查了贝叶斯和非贝斯参数估计问题，其中必要的参数概率密度被可用的数据替换。我们提出了各种数据驱动的版本，这些版本要么导致最佳估计量的神经网络近似值，要么在定义明确的优化问题中可以通过数值解决。特别是，对于数据驱动的相当于非bayesian估计的等效方面，我们最终得到了与生成网络设计相似的优化问题。
Optimum parameter estimation methods require knowledge of a parametric probability density that statistically describes the available observations. In this work we examine Bayesian and non-Bayesian parameter estimation problems under a data-driven formulation where the necessary parametric probability density is replaced by available data. We present various data-driven versions that either result in neural network approximations of the optimum estimators or in well defined optimization problems that can be solved numerically. In particular, for the data-driven equivalent of non-Bayesian estimation we end up with optimization problems similar to the ones encountered for the design of generative networks.
