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DTSTAMP:20260720T153749Z
DESCRIPTION:Title: Hierarchical Bayesian Modelling for Wireless Cellular Ne
 tworks.\n\n\n	Abstract:\n\n\nWith the recent advances in wireless technolog
 ies\, base stations are becoming more sophisticated. The network operators
  are also able to collect more data to improve network performance and use
 r experience. In this paper we concentrate on modeling performance of wire
 less cells using hierarchical Bayesian modeling framework. This framework 
 provides a principled way to navigate the space between the option of crea
 ting one model to represent all cells in a network and the option of creat
 ing separate models at each cell. The former option ignores the variations
  between cells (complete pooling) whereas the latter is overly noisy and i
 gnores the common patterns in cells (no pooling). The hierarchical Bayesia
 n model strikes a trade-off between these two extreme cases and enables us
  to do partial pooling of the data from all cells. This is done by estimat
 ing a parametric population distribution and assuming that each cell is a 
 sample from this distribution. Because this model is fully Bayesian\, it p
 rovides uncertainty intervals around each estimated parameter which can be
  used by network operators making network management decisions. We examine
  the performance of this method on a synthetic dataset and a real dataset 
 collected from a cellular network.\n
DTSTART:20190315T193000Z
DTEND:20190315T203000Z
LOCATION:Room 1205\, Burnside Hall\, CA\, QC\, Montreal\, H3A 0B9\, 805 rue
  Sherbrooke Ouest
SUMMARY:Dr. Deniz Ustebay 
URL:https://www.mcgill.ca/mathstat/channels/event/dr-deniz-ustebay-295379
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