Brunel University Research Archive(BURA) preserves and enables easy and open access to all
types of digital content. It showcases Brunel's research outputs.
Research contained within BURA is open access, although some publications may be subject
to publisher imposed embargoes. All awarded PhD theses are also archived on BURA.
Browsing by Subject covariance kernel parametrisation
Showing results 1 to 2 of 2
| Issue Date | Title | Author(s) |
|---|---|---|
| 16-Apr-2018 | Deep Bayesian Supervised Learning given Hypercuboidally-shaped, Discontinuous Data, using Compound Tensor-Variate & Scalar-Variate Gaussian Processes | Wang, K; Chakrabarty, D |
| 18-Apr-2024 | A New Reliable & Parsimonious Learning Strategy Comprising Two Layers of Gaussian Processes, to Address Inhomogeneous Empirical Correlation Structures | Roy, G; Chakrabarty, D |