  Integration over non-negative integrands is a central problem in machine
learning (e.g. for model averaging, (hyper-)parameter marginalisation, and
computing posterior predictive distributions). Bayesian Quadrature is a
probabilistic numerical integration technique that performs promisingly when
compared to traditional Markov Chain Monte Carlo methods. However, in contrast
to easily-parallelised MCMC methods, Bayesian Quadrature methods have, thus
far, been essentially serial in nature, selecting a single point to sample at
each step of the algorithm. We deliver methods to select batches of points at
each step, based upon those recently presented in the Batch Bayesian
Optimisation literature. Such parallelisation significantly reduces computation
time, especially when the integrand is expensive to sample.
