<mets:mets OBJID="eprint_13704" LABEL="Eprints Item" xsi:schemaLocation="http://www.loc.gov/METS/ http://www.loc.gov/standards/mets/mets.xsd http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-3.xsd" xmlns:mets="http://www.loc.gov/METS/" xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><mets:metsHdr CREATEDATE="2024-01-01T21:07:33Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>IIASA Repository</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_13704_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>CONVERGENCE OF BAYESIAN ESTIMATIONS IN ADAPTIVE CONTROL SCHEMES.</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">A.</mods:namePart><mods:namePart type="family">Yashin</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>This paper is devoted to the problem of finding strong consistency conditions for denumerable and uncountable sets of parameter values in the continuous time stochastic observation process. It turns out that the consistency property is often equivalent to the property of absolute continuity and singularity for some special family of probabilistic measures. The case of the uncountable set of parameter values yields more restraints on the links between unknown parameters and the observation process.</mods:abstract><mods:originInfo><mods:dateIssued encoding="iso8601">1982-10</mods:dateIssued></mods:originInfo><mods:genre>Article</mods:genre></mets:xmlData></mets:mdWrap></mets:dmdSec><mets:amdSec ID="TMD_eprint_13704"><mets:rightsMD ID="rights_eprint_13704_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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