  We present a novel approach to Bayesian inference and general Bayesian
computation that is defined through a sequential decision loop. Our method
defines a recursive partitioning of the sample space. It neither relies on
gradients nor requires any problem-specific tuning, and is asymptotically exact
for any density function with a bounded domain. The output is an approximation
to the whole density function including the normalisation constant, via
partitions organised in efficient data structures. Such approximations may be
used for evidence estimation or fast posterior sampling, but also as building
blocks to treat a larger class of estimation problems. The algorithm shows
competitive performance to recent state-of-the-art methods on synthetic and
real-world problems including parameter inference for gravitational-wave
physics.
