(variety of pizzas, salad, drinks provided)

Lunch introductions and discussion:

• Overview of the Center - Wojtek Szpankowski, Center Director
• Brief remarks on participation in NSF site visit, Dec 3-4 - Bob Brown, Managing Director
• Brief remarks on the education program - Brent Ladd, Education Director
• Students will introduce themselves and their research interests
• Open discussions and networking

Large Alphabet Compression and Predictive Distributions through Poissonization and Tilting

This talk introduces a convenient strategy for coding and predicting sequences of independent, identically distributed random variables generated from a large alphabet of size m. 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.