A framework is set up in which linear regression, as a way of approximating a random variable by other random variables, can be carried out in a variety of ways, which moreover ca...
R. Tyrrell Rockafellar, Stan Uryasev, Michael Zaba...
— The problem of statistical learning is to construct a predictor of a random variable Y as a function of a related random variable X on the basis of an i.i.d. training sample fr...
Statistical quantities, such as expectation (mean) and variance, play a vital role in the present age probabilistic analysis. In this paper, we present some formalization of expect...
General deviation measures are introduced and studied systematically for their potential applications to risk management in areas like portfolio optimization and engineering. Such...
R. Tyrrell Rockafellar, Stan Uryasev, Michael Zaba...
There exists a positive constant < 1 such that for any function T(n) n and for any problem L BPTIME(T(n)), there exists a deterministic algorithm running in poly(T(n)) time w...
We consider the problem of estimating the small probability that a function of a finite number of random variables exceeds a large threshold. Each input random variable may be lig...
In this paper, a new method is proposed in order to evaluate the stochastic solution of linear random differential equation. The method is based on the combination of the probabili...
Dynamic programming is introduced to quantize a continuous random variable into a discrete random variable. Quantization is often useful before statistical analysis or reconstruct...
Mingzhou (Joe) Song, Robert M. Haralick, Sté...
We present PROUD - A PRObabilistic approach to processing similarity queries over Uncertain Data streams, where the data streams here are mainly time series streams. In contrast t...
Mi-Yen Yeh, Kun-Lung Wu, Philip S. Yu, Ming-Syan C...
Given a random set coming from the imprecise observation of a random variable, we study how to model the information about the distribution of this random variable. Specifically,...