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In recent years the two trends of edge computing and artificial intelligencebecame both crucial for information processing infrastructures. While thecentralized analysis of massive amounts of data seems to be at odds withcomputation on the outer edge of distributed systems, we explore the propertiesof eventually consistent systems and statistics to identify sound formalismsfor probabilistic inference on the edge. In particular we treat time itself asa random variable that we incorporate into statistical models throughprobabilistic programming.

Techreport: Time-sensitive probabilistic inference for the edge