Default Clustering in Large Pools: Large Deviations

Large Deviations for Large Capacity

In this paper, we study large deviations of large capacity loss networks with xed routing. We use two-level modelling for the loss networks: the call level and the cell level. At the call level, a call request is accepted if it succeeds an admission test. The test is based on a polyhedral set of the number of calls in progress when a new call arrives. After being accepted, a call then transmits...

Large Deviations

Large Deviations

This paper is based on Wald Lectures given at the annual meeting of the IMS in Minneapolis during August 2005. It is a survey of the theory of large deviations. 1. Large deviations for sums. The role of " large deviations " is best understood through an example. Suppose that X random variables, for instance, normally distributed with mean zero and variance 1. Then, E e θ(X 1 +···+X n) = E[e θX ...

Combining Visualization and Interactive Clustering for Exploring Large Document Pools

In this work we present a visual method for exploration of huge document pools. Our method is based on topological visualization with Self-organizing map and document clustering to categories, combining advantages of both concepts. Proposed method is interactive, the user can explore document pool with two different interaction modalities from the perspective he is interested in and with differ...

Sample - Path Large Deviations

This paper considers Gaussian flows multiplexed in a queueing network. A single node being a useful but often incomplete setting, we examine more advanced models. We focus on a (twonode) tandem queue, fed by a large number of Gaussian inputs. With service rates and buffer sizes at both nodes scaled appropriately, Schilder’s sample-path large deviations theorem can be applied to calculate the as...