Feature allocations, probability functions, and paintboxes
The problem of inferring a clustering of a data set has been the subject of much research in Bayesian analysis, and there currently exists a solid mathematical foundation for Bayesian approaches to clustering. In particular, the class of probability distributions over partitions of a data set has been characterized in a number of ways, including via exchangeable partition probability functions (EPPFs) and the Kingman paintbox. Here, we develop a generalization of the clustering problem, called feature allocation, where we allow each... Read more about MachineLearn: Tamara Broderick
There will be 10 minute talks on a wide variety of future directions in statistics by Hadley Wickham (Chief Scientist at RStudio and creator of ggplot2), Hongkai Ji (Johns Hopkins professor and Harvard Stat alumnus), Sinan Aral (MIT), Daniela Witten (Univ of Washington), Hilary Mason (Data Scientist in Residence at Accel & co-founder of HackNY) and Joe Blitzstein (Harvard Stat professor). Here is more publicity.