The prevalence of technologies in the space of the Internet of Things and use of multi-processing computing platforms to aid in the computation required to perform learning and inference from large volumes of data has necessitated the extensive study of algorithms on decentralized platforms. In these settings, computing nodes send and receive data across graph-structured communication links, and using a combination of local computation and consensus-seeking communication, cooperately solve a problem of interest. Recently, Langevin dynamics as a tool for high dimensional sampling and posterior Bayesian inference has been studied in the context of a decentralized operation. However, this work has been limited to undirected graphs, wherein all communication is two-sided, i.e., if node A can send data to node B, then node B can also send data to node A. We extend the state of the art in considering Langevin dynamics on directed graphs.
翻译:由于在物联网空间使用各种技术以及使用多处理计算平台帮助计算进行大量数据学习和推断所需的计算,因此有必要对分散平台的算法进行广泛研究,在这些环境中,计算节点发送和接收跨图形结构通信链接的数据,并使用本地计算和寻求共识的通信相结合,合作解决了一个令人感兴趣的问题。最近,在分散作业的背景下,研究了作为高维取样和后子贝叶西亚推论工具的朗埃文动态,但这项工作仅限于非定向图表,其中所有通信都是双向的,即,如果节点A能够向节点B发送数据,那么节点B也可以向节点A发送数据。我们扩展了在定向图表上考虑朗埃文动态的艺术状况。