论文标题
深层基金推断
Deep Fiducial Inference
论文作者
论文摘要
自2000年代中期以来，人们一直在对现代信托推理的现代修改产生兴趣。迄今为止，提取广义基准分布的主要计算工具是马尔可夫链蒙特卡洛（MCMC）。我们提出了一种计算可以在复杂情况下使用的广义基准分布的替代方法。特别是，为了克服非标准的基准密度（MCMC所需）时的难度，我们设计了一个基准自动编码器（FAE）。拟合的自动编码器用于生成未知参数的广义基准样本。为了提高准确性，我们通过拒绝插入解码器时的样品，应用于近似的基准计算（AFC）算法，不能很好地复制观察到的数据。我们的数值实验显示了我们基于FAE的逆溶液的有效性以及AFC校正FAE溶液的出色覆盖范围。
Since the mid-2000s, there has been a resurrection of interest in modern modifications of fiducial inference. To date, the main computational tool to extract a generalized fiducial distribution is Markov chain Monte Carlo (MCMC). We propose an alternative way of computing a generalized fiducial distribution that could be used in complex situations. In particular, to overcome the difficulty when the unnormalized fiducial density (needed for MCMC), we design a fiducial autoencoder (FAE). The fitted autoencoder is used to generate generalized fiducial samples of the unknown parameters. To increase accuracy, we then apply an approximate fiducial computation (AFC) algorithm, by rejecting samples that when plugged into a decoder do not replicate the observed data well enough. Our numerical experiments show the effectiveness of our FAE-based inverse solution and the excellent coverage performance of the AFC corrected FAE solution.
