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English(EN) On the approximation of posterior laws in compound loss models by conditional Wasserstein GANs

新的 cWGAN 方法近似复合损失模型中的后验律

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种条件 Wasserstein 生成对抗网络 (cWGAN) 来近似复合损失模型中的后验律。这种方法允许单个生成器在各种先验分布下近似泊松强度和帕累托形状等参数的后验律。该方法通过基于模拟的校准、与解析后验和 MCMC 模拟的比较进行了验证，并应用于自然灾害损失数据以分析聚合尾部风险。 AI

影响 为量化金融中复杂的贝叶斯推理任务引入了一种新颖的生成对抗网络方法。

排序理由 详细介绍新统计方法的学术论文。 [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的 cWGAN 方法近似复合损失模型中的后验律

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详细介绍新统计方法的学术论文。 [lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Aleksandar Arandjelovic, Pavel V. Shevchenko, George Tzougas ·

    条件Wasserstein GANs对复合损失模型后验律的近似

    arXiv:2608.27229v1 Announce Type: cross Abstract: Bayesian inference in compound loss models must often be repeated across policies, market scenarios, and prior specifications. Outside conjugate cases, this may require repeated numerical integration or Markov chain Monte Carlo (M…

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