Non-ideal Boson System in the Gaussian Approximation

Generalized Ideal Parent (GIP): Discovering non-Gaussian Hidden Variables

A formidable challenge in uncertainty modeling in general, and when learning Bayesian networks in particular, is the discovery of unknown hidden variables. Few works that tackle this task are typically limited to discrete or Gaussian domains, or to tree structures. We propose a novel approach for discovering hidden variables in flexible nonGaussian domains using the powerful class of Gaussian c...

The Interacting Boson Approximation Model and Applications

Joint low-rank approximation for extracting non-Gaussian subspaces

In this article, we consider high-dimensional data which contains a low-dimensional non-Gaussian structure contaminated with Gaussian noise. Motivated by the joint diagonalization algorithms, we propose a linear dimension reduction procedure called joint low-dimensional approximation (JLA) to identify the non-Gaussian subspace. The method uses matrices whose non-zero eigen spaces coincide with ...

The Variational Gaussian Approximation Revisited

The variational approximation of posterior distributions by multivariate gaussians has been much less popular in the machine learning community compared to the corresponding approximation by factorizing distributions. This is for a good reason: the gaussian approximation is in general plagued by an Omicron(N)(2) number of variational parameters to be optimized, N being the number of random vari...

Conditions Of Gaussian Non-Markov Approximation For Multi-Channel Networks

In this paper the asymptotic analysis of multi-channel queueing networks is developed. Service processes in a heavy traffic regime for [ | , and -models are considered. Functional limit theorems of diffusion approximation type for service processes are formulated under convergence in uniform topology. A limit non-Markov Gaussian process is constructed, its correlation characteristics are repres...