The Transition from A Priori to A Posteriori Information: Bayesian Procedures in Distributed Large-Scale Data Processing Systems

Abstract

The procedure of transition from a priori to a posteriori information for a linear experiment in the context of Big Data systems is considered. At first glance, this process is fundamentally sequential, namely: as a result of observation, a priori information is transformed into a posteriori information, which is later interpreted as a priori for the next observation, etc. It is shown that such a procedure can be parallelized and unified due to the transformation of both the measurement results and the original a priori information into some special type. The properties of various forms of information representation are studied and compared. This approach makes it possible to effectively scale the Bayesian estimation procedure and, thus, adapt it to the problems of processing large amounts of distributed data.