Abstract
In this contribution, new online EM algorithms are proposed to perform inference in general hidden Markov models. These algorithms update the parameter at some deterministic times and use Sequential Monte Carlo methods to compute approximations of filtering distributions. Their convergence properties are addressed in [9] and [10]. In this paper, the performance of these algorithms are highlighted in the challenging framework of Simultaneous Localization and Mapping.
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Le Corff, S., Fort, G., Moulines, E. (2012). New Online EM Algorithms for General Hidden Markov Models. Application to the SLAM Problem. In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2012. Lecture Notes in Computer Science, vol 7191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28551-6_17
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DOI: https://doi.org/10.1007/978-3-642-28551-6_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28550-9
Online ISBN: 978-3-642-28551-6
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