  Solving decision problems in complex, stochastic environments is often
achieved by estimating the expected outcome of decisions via Monte Carlo
sampling. However, sampling may overlook rare, but important events, which can
severely impact the decision making process. We present a method in which a
Normalizing Flow generative model is trained to simulate samples directly from
a conditional distribution given that a rare event occurs. By utilizing
Coupling Flows, our model can, in principle, approximate any sampling
distribution arbitrarily well. By combining the approximation method with
Importance Sampling, highly accurate estimates of complicated integrals and
expectations can be obtained. We include several examples to demonstrate how
the method can be used for efficient sampling and estimation, even in
high-dimensional and rare-event settings. We illustrate that by simulating
directly from a rare-event distribution significant insight can be gained into
the way rare events happen.
