Large deviations

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 ...

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...

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...

Large deviations for mixtures

Suppose the distributions {μn} on Θ obey a large deviation principle (LDP) and that for each θ ∈ Θ the distributions {Pn θ } on X also obey an LDP. The main purpose of this paper is to give conditions which allow an LDP for the mixtures {Pn}, given by Pn(A) = ∫ Pn θ (A)dμn(θ), to be deduced. The treatment follows that of Dinwoodie and Zabell (1992) who, motivated by exchangeability, consider th...