In all systems simulation, random variates are considered as a main factor and based of simulation heart. Actually, randomization is inducted by random variates in the simulation. Due to the importance of such a problem, a new method for generation of random variates from continuous distributions is presented in this paper. The proposed algorithm, called uniform fractional part (UFP) is simpler...

This work develops a new multifidelity ensemble Kalman filter (MFEnKF) algorithm based on linear control variate framework. The approach allows for rigorous extensions of the EnKF, ...

We show how regional prediction of car insurance risks can be improved for finer subregions by combining explanatory modeling with phenomenological models from industrial practice. Motivated by the control-variates technique, we propose a suitable combined predictor when claims data are available for regions but not for subregions. We provide explicit conditions which imply that the mean square...

This paper reports some initial investigations of the use of antithetic variates in perfect sampling. A simple random walk example is presented to illustrate the key ingredients of antithetic coupling for perfect sampling as well as its potential benefit. A key step in implementing antithetic coupling is to generate random variates that are negatively associated, a stronger condition than negat...

In this paper, control variates are proposed to speed up Monte Carlo Simulations to estimate expected error rates in multivariate classiication.

Generalizability in a multi-subject fMRI study is investigated. The analysis is based on principal and independent component representations. Subsequent supervised learning and classification is carried out by canonical variates analysis and clustering methods. The generalization error is estimated by cross-validation, forming the so-called learning curves. The fMRI case story is a motor-contro...

Price's theorem is derived for complex valued variates. The derivation differs from the existing derivation in two respects. First, the normal variates are not assumed to be circularly complex. Thus the result is more general. Second, the characteristic function of the complex variates is not used.