Validation by Monte Carlo sampling of experimental observation functionals

Efficient Monte Carlo sampling by parallel marginalization.

Markov chain Monte Carlo sampling methods often suffer from long correlation times. Consequently, these methods must be run for many steps to generate an independent sample. In this paper, a method is proposed to overcome this difficulty. The method utilizes information from rapidly equilibrating coarse Markov chains that sample marginal distributions of the full system. This is accomplished th...

Control Functionals for Quasi-Monte Carlo Integration

Quasi-Monte Carlo (QMC) methods are being adopted in statistical applications due to the increasingly challenging nature of numerical integrals that are now routinely encountered. For integrands with d-dimensions and derivatives of order α, an optimal QMC rule converges at a best-possible rate O(N−α/d). However, in applications the value of α can be unknown and/or a rate-optimal QMC rule can be...

Monte Carlo cross validation

In order to choose correctly the dimension of calibration model in chemistry, a new simple and effective method named Ž . Monte Carlo cross validation MCCV is introduced in the present work. Unlike leave-one-out procedure commonly used in Ž . chemometrics for cross validation CV , the Monte Carlo cross validation developed in this paper is an asymptotically consistent method in determining the ...

Christiane Lemieux Monte Carlo and Quasi - Monte Carlo Sampling

Design of Sparse Linear Arrays by Monte Carlo Importance Sampling

The formation of acoustic images in real-time requires an enormous computational burden. To reduce this demand the use of sparse arrays for beamforming is mandated. The design of these arrays for adequate mainlobe width and low sidelobe level is a difficult nonlinear optimization problem. A new approach to the joint optimization of sensor placement and shading weights is discussed. Based on the...