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  <abstract type="Summary">This thesis focuses on Bayesian filtering for systems that consist of multiple, interacting entites (e.g. agents or objects), which can naturally be described by Multiset Rewriting Systems (MRSs). The main insight is that the state space that is underling an MRS exhibits a certain symmetry, which can be exploited to increase inference efficiency. We provide an efficient, lifted filtering algorithm, which is able to achieve a factorial reduction in space and time complexity, compared to conventional, ground filtering.&lt;eng&gt;</abstract>
  <abstract type="Summary">Diese Arbeit betrachtet Bayes'sche Filter in Systemen, die aus mehreren, interagierenden Entitäten (z.B. Agenten oder Objekten) bestehen. Die Systemdynamik solcher Systeme kann auf natürliche Art durch Multiset Rewriting Systems (MRS) spezifiziert werden. Die wesentliche Erkenntnis ist, dass der Zustandraum Symmetrien aufweist, die ausgenutzt werden können, um die Effizienz der Inferenz zu erhöhen. Wir führen einen effizienten, gelifteten Filter-Algorithmus ein, dessen Zeit- und Platzkomplexität gegenüber dem grundierten Algorithmus um einen faktoriellen Faktor reduziert ist.&lt;ger&gt;</abstract>
  <note type="statement of responsibility">vorgelegt von Stefan Lüdtke</note>
  <note>GutachterInnen: Thomas Kirste (Universität Rostock) ; Heiner Stuckenschmidt (Universität Mannheim)</note>
  <note type="thesis">Dissertation Universität Rostock 2021</note>
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    <titleInfo>
      <title>Lifted Bayesian filtering in multi-entity systems</title>
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      <publisher>Rostock, 2020</publisher>
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      <form>xi, 195 Seiten</form>
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      <namePart>Lüdtke, Stefan, 1991 - </namePart>
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