标题： 变分贝叶斯用于合并噪声数据库 显示英文标题

标题： Variational Bayes for Merging Noisy Databases

摘要： 贝叶斯实体解析将多个噪声数据库合并，并返回表示的唯一个体的最小集合，以及它们真实的潜在记录值。 贝叶斯方法允许灵活的生成模型，这些模型在数据库之间共享能力，并对最终解析后的数据库查询进行合理的不确定性量化。 然而，现有的贝叶斯实体解析方法使用马尔可夫蒙特卡罗方法（MCMC）近似，对于包含数百万或数十亿条记录的现代数据库来说速度太慢。 相反，我们提出应用变分近似方法，以在这些模型中实现可扩展的贝叶斯推理。 我们推导了用于均场变分贝叶斯的坐标上升近似，定性地将我们的算法与现有方法进行比较，指出在实体解析中从聚类大小的期望分布中出现的独特的推理挑战，并讨论该领域未来工作的方向。 显示英文摘要

摘要： Bayesian entity resolution merges together multiple, noisy databases and returns the minimal collection of unique individuals represented, together with their true, latent record values. Bayesian methods allow flexible generative models that share power across databases as well as principled quantification of uncertainty for queries of the final, resolved database. However, existing Bayesian methods for entity resolution use Markov monte Carlo method (MCMC) approximations and are too slow to run on modern databases containing millions or billions of records. Instead, we propose applying variational approximations to allow scalable Bayesian inference in these models. We derive a coordinate-ascent approximation for mean-field variational Bayes, qualitatively compare our algorithm to existing methods, note unique challenges for inference that arise from the expected distribution of cluster sizes in entity resolution, and discuss directions for future work in this domain.