Efficient numerical models for investigating the statistics of random dynamic systems

The study of random dynamic systems usually requires the definition of an ensemble of structures and the solution of the eigenproblem for each member of the ensemble. If the process is carried out using a conventional numerical approach, the computational cost becomes prohibitive for complex systems. In this work, an alternative numerical method is proposed. The results for the response statistics are compared with values obtained from a detailed stochastic FE analysis of plates. The proposed method seems to capture the statistical behaviour of the response with a reduced computational cost.

Efficient statistical electromagnetic analysis in reverberant environments

This paper presents a statistical approach to the electromagnetic analysis of a system that lies within a reverberant cavity that has random or uncertain properties. The need to solve Maxwell's equations within the cavity is avoided by employing a relation known as the diffuse field reciprocity principle, which leads directly to the ensemble mean squared response of the system; all that is required is the impedance matrix of the system associated with radiation into infinite space. The general theoretical approach is presented, and the analysis is then applied to a five-cable bundle in a reverberation room © 2013 EMC Europe Foundation.

The Random Forest Kernel and other kernels for big data from random partitions

We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. We show how the construction can be used to create kernels from methods that would not normally be viewed as random partitions, such as Random Forest. To demonstrate the potential of this method, we propose two new kernels, the Random Forest Kernel and the Fast Cluster Kernel, and show that these kernels consistently outperform standard kernels on problems involving real-world datasets. Finally, we show how the form of these kernels lend themselves to a natural approximation that is appropriate for certain big data problems, allowing $O(N)$ inference in methods such as Gaussian Processes, Support Vector Machines and Kernel PCA.

A derivation of the asymptotic random-coding prefactor

This paper studies the subexponential prefactor to the random-coding bound for a given rate. Using a refinement of Gallager's bounding techniques, an alternative proof of a recent result by Altuǧ and Wagner is given, and the result is extended to the setting of mismatched decoding. © 2013 IEEE.