The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling
methods defined on the Riemann manifold to resolve the shortcomings of existing Monte …

Psychiatry faces fundamental challenges with regard to mechanistically guided differential
diagnosis, as well as prediction of clinical trajectories and treatment response of individual …

Studies of evolution at the molecular level have experienced phenomenal growth in the last
few decades, due to rapid accumulation of genetic sequence data, improved computer …

A non-parametric extension of control variates is presented. These leverage gradient
information on the sampling density to achieve substantial variance reduction. It is not …

We present a detection of 21 cm emission from large-scale structure (LSS) between redshift
0.78 and 1.43 made with the Canadian Hydrogen Intensity Map** Experiment. Radio …

Although countless highly penetrant variants have been associated with Mendelian
disorders, the genetic etiologies underlying complex diseases remain largely unresolved. By …

The model evidence is a vital quantity in the comparison of statistical models under the
Bayesian paradigm. This study presents a review of commonly used methods. We outline …

Neuroimaging increasingly exploits machine learning techniques in an attempt to achieve
clinically relevant single-subject predictions. An alternative to machine learning, which tries …

A research frontier has emerged in scientific computation, wherein discretisation error is
regarded as a source of epistemic uncertainty that can be modelled. This raises several …

Motivation: High-dimensional single-cell snapshot data are becoming widespread in the
systems biology community, as a mean to understand biological processes at the cellular …