On Spectrum Estimation of a Clipped Gaussian Random Process

the impact of portfolio assessment on iranian efl students essay writing: a process-oriented approach

this study was conducted to investigate the impact of portfolio assessment as a process-oriented assessment mechanism on iranian efl students’ english writing and its subskills of focus, elaboration, organization, conventions, and vocabulary. out of ninety juniors majoring in english literature and translation at the university of isfahan, sixty one of them who were at the same level of writing...

Gaussian Process Random Fields

Gaussian processes have been successful in both supervised and unsupervised machine learning tasks, but their computational complexity has constrained practical applications. We introduce a new approximation for large-scale Gaussian processes, the Gaussian Process Random Field (GPRF), in which local GPs are coupled via pairwise potentials. The GPRF likelihood is a simple, tractable, and paralle...

Autoregressive Gaussian Random Vectors of First Order

Sparse Spectrum Gaussian Process Regression

We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsify the spectral representation of the GP. This leads to a simple, practical algorithm for regression tasks. We compare the achievable trade-offs between predictive accuracy and computational requirements, and show that these are typically superior to existing state-of-the-art sparse approximations...

Gaussian Process Latent Random Field

The Gaussian process latent variable model (GPLVM) is an unsupervised probabilistic model for nonlinear dimensionality reduction. A supervised extension, called discriminative GPLVM (DGPLVM), incorporates supervisory information into GPLVM to enhance the classification performance. However, its limitation of the latent space dimensionality to at most C − 1 (C is the number of classes) leads to ...