论文标题
大约适合和比较保险损失模型的贝叶斯计算
Approximate Bayesian Computations to fit and compare insurance loss models
论文作者
论文摘要
近似贝叶斯计算（ABC）是一种统计学习技术，可以通过将观察到的数据与模拟数据进行比较来校准和选择模型。该技术绕过了可能性的使用，只需要从感兴趣模型中生成合成数据的能力。我们使用ABC使用汇总数据适合和比较保险损失模型。提出了Python中最先进的ABC实施。它使用顺序的蒙特卡洛（Monte Carlo）从后部分布和Wasserstein距离进行取样，以比较观察到的和合成数据。
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.
