<mets:mets OBJID="eprint_14150" 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="2024-01-01T20:50:26Z"><mets:agent ROLE="CUSTODIAN" TYPE="ORGANIZATION"><mets:name>IIASA Repository</mets:name></mets:agent></mets:metsHdr><mets:dmdSec ID="DMD_eprint_14150_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:titleInfo><mods:title>On stochastic filtering approximations of estimation problems for systems with uncertainty∗</mods:title></mods:titleInfo><mods:name type="personal"><mods:namePart type="given">A.</mods:namePart><mods:namePart type="family">Kurzhanski</mods:namePart><mods:role><mods:roleTerm type="text">author</mods:roleTerm></mods:role></mods:name><mods:abstract>This paper considers estimating the state of a discrete-time linear system in which the values of an unobserved disturbance process are only known to lie in certain prescribed sets. By introducing additional stochastic disturbances it is shown that this problem can be approximated to arbitrarily high accuracy by the solution of a Kalman filtering problem</mods:abstract><mods:originInfo><mods:dateIssued encoding="iso8601">1988</mods:dateIssued></mods:originInfo><mods:originInfo><mods:publisher>Taylor and Francis</mods:publisher></mods:originInfo><mods:genre>Article</mods:genre></mets:xmlData></mets:mdWrap></mets:dmdSec><mets:amdSec ID="TMD_eprint_14150"><mets:rightsMD ID="rights_eprint_14150_mods"><mets:mdWrap MDTYPE="MODS"><mets:xmlData><mods:useAndReproduction>
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