  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.
