The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …

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

Many spatially resolved transcriptomic technologies do not have single-cell resolution but
measure the average gene expression for each spot from a mixture of cells of potentially …

Identifying genes that display spatial expression patterns in spatially resolved transcriptomic
studies is an important first step toward characterizing the spatial transcriptomic landscape …

To date, several methods exist in the statistical literature for model assessment, which
purport themselves specifically as Bayesian predictive methods. The decision theoretic …

GPstuff: Bayesian Modeling with Gaussian Processes Page 1 Journal of Machine Learning
Research 14 (2013) 1175-1179 Submitted 6/12; Revised 10/12; Published 4/13 GPstuff …

This paper proposes a novel scheme for reduced-rank Gaussian process regression. The
method is based on an approximate series expansion of the covariance function in terms of …

Biomedical research typically involves longitudinal study designs where samples from
individuals are measured repeatedly over time and the goal is to identify risk factors …

Maps of infectious disease—charting spatial variations in the force of infection, degree of
endemicity and the burden on human health—provide an essential evidence base to …

Species distribution modelling (SDM) is widely used in ecology, and predictions of species
distributions inform both policy and ecological debates. Therefore, methods with high …