  Simulation-based inference (SBI) solves statistical inverse problems by
repeatedly running a stochastic simulator and inferring posterior distributions
from model-simulations. To improve simulation efficiency, several inference
methods take a sequential approach and iteratively adapt the proposal
distributions from which model simulations are generated. However, many of
these sequential methods are difficult to use in practice, both because the
resulting optimisation problems can be challenging and efficient diagnostic
tools are lacking. To overcome these issues, we present Truncated Sequential
Neural Posterior Estimation (TSNPE). TSNPE performs sequential inference with
truncated proposals, sidestepping the optimisation issues of alternative
approaches. In addition, TSNPE allows to efficiently perform coverage tests
that can scale to complex models with many parameters. We demonstrate that
TSNPE performs on par with previous methods on established benchmark tasks. We
then apply TSNPE to two challenging problems from neuroscience and show that
TSNPE can successfully obtain the posterior distributions, whereas previous
methods fail. Overall, our results demonstrate that TSNPE is an efficient,
accurate, and robust inference method that can scale to challenging scientific
models.
