The future development of quantum technologies relies on creating and manipulating
quantum systems of increasing complexity, with key applications in computation, simulation …

Recently, increased computational power and data availability, as well as algorithmic
advances, have led machine learning (ML) techniques to impressive results in regression …

PyMC is a probabilistic programming library for Python that provides tools for constructing
and fitting Bayesian models. It offers an intuitive, readable syntax that is close to the natural …

The use of machine learning in computational molecular design has great potential to
accelerate the discovery of innovative materials. However, its practical benefits still remain …

Climate projections continue to be marred by large uncertainties, which originate in
processes that need to be parameterized, such as clouds, convection, and ecosystems. But …

Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …

We fitted a model of SARS-CoV-2 transmission in care homes and the community to
regional surveillance data for England. Compared with other approaches, our model …

History Markov chain Monte Carlo (MCMC) methods, in which plausible values for unknown
quantities are simulated from their appropriate probability distribution, have revolutionised …

Particle filters contain the promise of fully nonlinear data assimilation. They have been
applied in numerous science areas, including the geosciences, but their application to high …

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