Sensor placement is an optimisation problem that has recently gained great relevance. In order to achieve accurate online updates of a predictive model, sensors are used provide observations. When sensor location optimally selected, the model can greatly reduce its internal errors. A greedy-selection algorithm for locating these optimal spatial locations from numerical embedded space. novel arc...

Abstract Learning from demonstration allows to encode task constraints observing the motion executed by a human teacher. We present Gaussian-process-based learning (LfD) approach that robots learn manipulation skills demonstrations of By exploiting potential Gaussian process (GP) models offer, we unify in single, entirely GP-based framework, main features required for state-of-the-art LfD appro...

We propose a novel sparse spectrum approximation of Gaussian process (GP) tailored for Bayesian optimization (BO). Whilst the current methods provide desired approximations regression problems, it is observed that this particular form generates an overconfident GP, i.e., produces less epistemic uncertainty than original GP. Since balance between predictive mean and variance key determinant to s...

Deep Gaussian processes (DGPs) provide a rich class of models that can better represent functions with varying regimes or sharp changes, compared to conventional GPs. In this work, we propose novel inference method for DGPs computer model emulation. By stochastically imputing the latent layers, our approach transforms DGP into linked GP: emulator developed systems models. This transformation pe...

In this paper, Gaussian Process Regression (GPR)-based models which use the Bayesian approach to regression analysis problem such as load forecasting (LF) are proposed. The GPR is a non-parametric kernel-based learning method having ability provide correct predictions with uncertainty in measurements. proposed model provides an hourly and monthly forecast for Australian city four Indian cities ...