  We analyse the performance of several iterative algorithms for the
quantisation of a probability measure $\mu$, based on the minimisation of a
Maximum Mean Discrepancy (MMD). Our analysis includes kernel herding, greedy
MMD minimisation and Sequential Bayesian Quadrature (SBQ). We show that the
finite-sample-size approximation error, measured by the MMD, decreases as $1/n$
for SBQ and also for kernel herding and greedy MMD minimisation when using a
suitable step-size sequence. The upper bound on the approximation error is
slightly better for SBQ, but the other methods are significantly faster, with a
computational cost that increases only linearly with the number of points
selected. This is illustrated by two numerical examples, with the target
measure $\mu$ being uniform (a space-filling design application) and with $\mu$
a Gaussian mixture.
