Beta负二项过程与可交换随机划分的混合隶属度模型
Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling
摘要：
采用整数值随机过程-β负二项过程（BNBP）将计数向量划分为潜在随机计数矩阵。由于支配分组数据的可交换随机分区的BNBP的边缘概率分布尚未被开发，BNBP的当前推断必须截断beta过程的原子数。本文引入了一个可交换分区的概率函数来明确地描述BNBP如何将每个组的数据点聚类成一个随机数的可交换分区，这些分区在所有组之间共享。一个完全崩溃的吉布斯采样器开发的BNBP，导致一种新的非参数贝叶斯主题模型，是不同于现有的，具有简单的实现，快速收敛，良好的混合，和国家的最先进的预测性能。
英文摘要：
The beta-negative binomial process (BNBP), an integer-valued stochastic process, is employed to partition a count vector into a latent random count matrix. As the marginal probability distribution of the BNBP that governs the exchangeable random partitions of grouped data has not yet been developed, current inference for the BNBP has to truncate the number of atoms of the beta process. This paper introduces an exchangeable partition probability function to explicitly describe how the BNBP clusters the data points of each group into a random number of exchangeable partitions, which are shared across all the groups. A fully collapsed Gibbs sampler is developed for the BNBP, leading to a novel nonparametric Bayesian topic model that is distinct from existing ones, with simple implementation, fast convergence, good mixing, and state-of-the-art predictive performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
