Publikation: Dissertationsschrift

Lifted Bayesian filtering in multi-entity systems

Grunddaten
Abstract
Autoren
Einrichtung

Grunddaten

Titel	Lifted Bayesian filtering in multi-entity systems
Erscheinungsjahr	2020
Verlagsort	Rostock
Seitenzahl	195
Publikationsform	Druckschrift
Publikationsart	Dissertationsschrift
Sprache	Englisch
Letzte Änderung	27.05.2021 06:02:08
Bearbeitungsstatus	durch UB Rostock abschließend validiert
Dauerhafte URL	http://purl.uni-rostock.de/fodb/pub/65778
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Abstract

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.

Autor

Lüdtke, Stefan

Einrichtung

Fakultät für Informatik und Elektrotechnik (IEF)