at the
American Institute of Mathematics, Palo Alto, California
organized by
Alon Orlitsky, Narayana Santhanam, and Krishnamurthy Viswanathan
This workshop, sponsored by AIM and the NSF, will study the problem of estimating a probability distribution from a small data sample it generates. The workshop will investigate consolidating a theoretical and algorithmic framework for this topic. Aspects addressed will include distribution, probability, and population estimation, prediction, and classification. Emphasis will be on recent methods related to Good-Turing estimation and patterns, which cast the problem in a combinatorial and machine-learning perspective and relate it to integer and set partitions, symmetric polynomials over many variables, computation of matrix permanents, Markov chain Monte Carlo techniques, universal compression, and geometric programming.
The workshop will differ from typical conferences in some regards. Participants will be invited to suggest open problems and questions before the workshop begins, and these will be posted on the workshop website. These include specific problems on which there is hope of making some progress during the workshop, as well as more ambitious problems which may influence the future activity of the field. Lectures at the workshop will be focused on familiarizing the participants with the background material leading up to specific problems, and the schedule will include discussion and parallel working sessions.
The deadline to apply for support to participate in this workshop has passed.
For more information email workshops@aimath.org
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