We introduce a novel framework for simultaneous structure and parameter learning in hidden-variable conditional probability models, based on an entropic prior and a solution for i...
This paper addresses the problem of computational error modeling and analysis. Choosing different word-lengths for each functional unit in hardware implementations of numerical al...
This paper addresses the following question: how should we update our beliefs after observing some incomplete data, in order to make credible predictions about new, and possibly i...
Abstract. The Parallel Disks Model (PDM) has been proposed to alleviate the I/O bottleneck that arises in the processing of massive data sets. Sorting has been extensively studied ...
Given discrete event data, we wish to produce a probability density that can model the relative probability of events occurring in a spatial region. Common methods of density esti...
Laura M. Smith, Matthew S. Keegan, Todd Wittman, G...