Estimating distributions varying in time in a universal manner
Proceedings of the IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017
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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.
Individual sequence manner
Sequential density estimation
Probability density function
Estimation of distributions
Probability density estimation
Time varying parameter
Published Version (Please cite this version)http://dx.doi.org/10.1109/SIU.2017.7960588
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