  As a special example of piecewise deterministic Markov process, bouncy
particle sampler is a rejection-free, irreversible Markov chain Monte Carlo
algorithm and can draw samples from target distribution efficiently. We
generalize bouncy particle sampler in terms of its transition dynamics. In BPS,
the transition dynamic at event time is deterministic, but in GBPS, it is
random. With the help of this randomness, GBPS can overcome the reducibility
problem in BPS without refreshement.
