  ABC algorithms involve a large number of simulations from the model of
interest, which can be very computationally costly. This paper summarises the
lazy ABC algorithm of Prangle (2015), which reduces the computational demand by
abandoning many unpromising simulations before completion. By using a random
stopping decision and reweighting the output sample appropriately, the target
distribution is the same as for standard ABC. Lazy ABC is also extended here to
the case of non-uniform ABC kernels, which is shown to simplify the process of
tuning the algorithm effectively.
