Approximate Bayesian Computation (abc) is a statistical learning technique to calibrate and select models by comparing observed data to simulated data. This technique bypasses the use of the likelihood and requires only the ability to generate synthetic data from the models of interest. We apply abc to fit and compare insurance loss models using aggregated data. A state-of-the-art abc implementation in Python is proposed. It uses sequential Monte Carlo to sample from the posterior distribution and the Wasserstein distance to compare the observed and synthetic data.

中文翻译：

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近似贝叶斯计算以拟合和比较保险损失模型

近似贝叶斯计算 ( abc ) 是一种统计学习技术，通过将观察数据与模拟数据进行比较来校准和选择模型。这种技术绕过了似然的使用，只需要能够从感兴趣的模型中生成合成数据。我们应用abc来拟合和比较使用汇总数据的保险损失模型。提出了在 Python 中最先进的abc实现。它使用顺序蒙特卡罗从后验分布和 Wasserstein 距离中采样，以比较观察到的和合成的数据。