Truncated Marginal Neural Ratio Estimation. (arXiv:2107.01214v1 [stat.ML])

Parametric stochastic simulators are ubiquitous in science, often featuring
high-dimensional input parameters and/or an intractable likelihood. Performing
Bayesian parameter inference in this context can be challenging. We present a
neural simulator-based inference algorithm which simultaneously offers
simulation efficiency and fast empirical posterior testability, which is unique
among modern algorithms. Our approach is simulation efficient by simultaneously
estimating low-dimensional marginal posteriors instead of the joint posterior
and by proposing simulations targeted to an observation of interest via a prior
suitably truncated by an indicator function. Furthermore, by estimating a
locally amortized posterior our algorithm enables efficient empirical tests of
the robustness of the inference results. Such tests are important for
sanity-checking inference in real-world applications, which do not feature a
known ground truth. We perform experiments on a marginalized version of the
simulation-based inference benchmark and two complex and narrow posteriors,
highlighting the simulator efficiency of our algorithm as well as the quality
of the estimated marginal posteriors. Implementation on GitHub.



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