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Regular version of the site

Book chapter

Information spaces: optimizing sequential and parallel processing in big data

P. 173-176.

The process of Bayesian information update is essentially sequential: as a result of observation, a prior information is transformed to a posterior, which is later interpreted as a prior for the next observation, etc. It is shown that this procedure can be unified and parallelized by converting both the measurement results and the original prior information to a special form. Various forms of information representation and relations between them are studied. Rich algebraic properties of the introduced canonical information space allow to efficiently scale Bayesian procedure and adapt it to processing large amounts of distributed data.