  We propose a general-purpose approximation to the Ferguson-Klass algorithm
for generating samples from L\'evy processes without Gaussian components. We
show that the proposed method is more than 1000 times faster than the standard
Ferguson-Klass algorithm without a significant loss of precision. This method
can open an avenue for computationally efficient and scalable Bayesian
nonparametric models which go beyond conjugacy assumptions, as demonstrated in
the examples section.
