Xiao (Grace) Yang - Large Alphabet Compression and Predictive Distributions through Poissonization and Tilting
Monday, October 07, 2013 1:30 PM - 2:30 PM EDT
This talk introduces a convenient strategy for coding and predicting sequences of independent, identically distributed random variables generated from a large alphabet of sizem. In particular, the size of the sample is allowed to be variable. The employment of a Poisson model and tilting method simplifies the implementation and analysis through independence. The resulting strategy is optimal within the class of distributions satisfying a moment condition, and is close to optimal for the class of all i.i.d distribution or strings of a given length. Moreover, the method can be used to code and predict sequences with a condition on the tail of the ordered counts. It can also be applied to distributions in an envelope class.