5 papers
Soft Random Graphs in Probabilistic Metric Spaces & Inter-graph Distance
Kangrui Wang, Dalia Chakrabarty
We present a new method for learning Soft Random Geometric Graphs (SRGGs), drawn in probabilistic metric spaces, with the connection function of the graph defined as the marginal p…
Multi-resolution Multi-task Gaussian Processes
Oliver Hamelijnck, Theodoros Damoulas, Kangrui Wang +1
We consider evidence integration from potentially dependent observation processes under varying spatio-temporal sampling resolutions and noise levels. We develop a multi-resolution…
Deep Bayesian Supervised Learning given Hypercuboidally-shaped, Discontinuous Data, using Compound Tensor-Variate & Scalar-Variate Gaussian Processes
Kangrui Wang, Dalia Chakrabarty
We undertake Bayesian learning of the high-dimensional functional relationship between a system parameter vector and an observable, that is in general tensor-valued. The ultimate a…
Bayesian Covariance Modelling of Large Tensor-Variate Data Sets Inverse Non-parametric Learning of the Unknown Model Parameter Vector
Kangrui Wang, Dalia Chakrabarty
Tensor-valued data are being encountered increasingly more commonly, in the biological, natural as well as the social sciences. The learning of the unknown model parameter vector g…
Uncertainty in Test Score Data and Classically Defined Reliability of Tests and Test Batteries, using a New Method for Test Dichotomisation
Satyendra Nath Chakrabartty, Kangrui Wang, Dalia Chakrabarty
As with all measurements, the measurement of examinee ability, in terms of scores that the examinee obtains in a test, is also error-ridden. The quantification of such error or unc…