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      • Department of Electrical and Electronics Engineering
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      Estimating distributions varying in time in a universal manner

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      Author(s)
      Gökçesu, Kaan
      Manış, Eren
      Kurt, Ali Emirhan
      Yar, Ersin
      Date
      2017
      Source Title
      Proceedings of the IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017
      Publisher
      IEEE
      Language
      Turkish
      Type
      Conference Paper
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      Abstract
      We investigate the estimation of distributions with time-varying parameters. We introduce an algorithm that achieves the optimal negative likelihood performance against the true probability distribution. We achieve this optimum regret performance without any knowledge about the total change of the parameters of true distribution. Our results are guaranteed to hold in an individual sequence manner such that we have no assumptions on the underlying sequences. Apart from the regret bounds, through synthetic and real life experiments, we demonstrate substantial performance gains with respect to the state-of-the-art probability density estimation algorithms in the literature.
      Keywords
      Exponential family
      Individual sequence manner
      Nonstationary source
      Sequential density estimation
      Probability density function
      Density estimation
      Estimation of distributions
      Probability density estimation
      Time varying parameter
      Probability distributions
      Permalink
      http://hdl.handle.net/11693/37584
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/SIU.2017.7960588
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      • Department of Electrical and Electronics Engineering 4016
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