论文标题
截断的模拟和推理边缘交换网络
Truncated Simulation and Inference in Edge-Exchangeable Networks
论文作者
论文摘要
边缘交换概率网络模型从离散度量中产生边缘作为I.I.D.〜序列，为潜在网络属性的统计推断提供了一种简单的手段。该度量通常是使用贝叶斯非参数（BNP）离散先验实现的自我产生的；但是，与标准BNP模型不同，先验的自我产品措施并不是偶联的可能性，从而阻碍了精确的模拟和推理算法的发展。通过离散度量的有限截断近似是一种直接的替代方案，但会导致未知的近似误差。在本文中，我们开发了基于截断的随机自我生产测量模型中正向模拟和后验推断的方法，并为结果的质量提供理论保证，这是截断水平的函数。我们提出的技术是一般的，并扩展到更广泛的离散贝叶斯非参数模型。
Edge-exchangeable probabilistic network models generate edges as an i.i.d.~sequence from a discrete measure, providing a simple means for statistical inference of latent network properties. The measure is often constructed using the self-product of a realization from a Bayesian nonparametric (BNP) discrete prior; but unlike in standard BNP models, the self-product measure prior is not conjugate the likelihood, hindering the development of exact simulation and inference algorithms. Approximation via finite truncation of the discrete measure is a straightforward alternative, but incurs an unknown approximation error. In this paper, we develop methods for forward simulation and posterior inference in random self-product-measure models based on truncation, and provide theoretical guarantees on the quality of the results as a function of the truncation level. The techniques we present are general and extend to the broader class of discrete Bayesian nonparametric models.
