The direct Gaussian copula model with discrete margins is appealing but poses computational challenges due to its intractable likelihood. We show that the distributional transform-based approximate likelihood is essentially exact for some variants of the model, and we propose a quantity that can be used to assess exactness for a given dataset.

中文翻译：

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具有离散余量的直接高斯copula模型的分布变换逼近的偶尔精度

具有离散余量的直接高斯copula模型很吸引人，但由于其难以解决的可能性而给计算带来了挑战。我们表明，基于分布变换的近似似然对于模型的某些变体基本上是精确的，并且我们提出了可用于评估给定数据集准确性的数量。