论文标题
部分可观测时空混沌系统的无模型预测
Bayesian Integrals on Toric Varieties
论文作者
论文摘要
我们探讨了在复曲面品种的环境中统计模型的积极几何形状。我们的重点在于用于通过COX坐标参数化的离散数据的模型。我们为贝叶斯统计数据中的计算开发了一种几何理论，例如评估边缘可能性积分和后分布的采样。这些基于一种热带抽样方法，用于评估物理学中的Feynman积分。在这里，我们将该方法从投影空间扩展到任意的感谢您。
We explore the positive geometry of statistical models in the setting of toric varieties. Our focus lies on models for discrete data that are parameterized in terms of Cox coordinates. We develop a geometric theory for computations in Bayesian statistics, such as evaluating marginal likelihood integrals and sampling from posterior distributions. These are based on a tropical sampling method for evaluating Feynman integrals in physics. We here extend that method from projective spaces to arbitrary toric varieties.
