  How can one perform Bayesian inference on stochastic simulators with
intractable likelihoods? A recent approach is to learn the posterior from
adaptively proposed simulations using neural network-based conditional density
estimators. However, existing methods are limited to a narrow range of proposal
distributions or require importance weighting that can limit performance in
practice. Here we present automatic posterior transformation (APT), a new
sequential neural posterior estimation method for simulation-based inference.
APT can modify the posterior estimate using arbitrary, dynamically updated
proposals, and is compatible with powerful flow-based density estimators. It is
more flexible, scalable and efficient than previous simulation-based inference
techniques. APT can operate directly on high-dimensional time series and image
data, opening up new applications for likelihood-free inference.
