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What should I do when my ‘posterior samples are outside of the prior support’ in SNPE?

When working with multi-round SNPE, you might have experienced the following warning:

Only x% posterior samples are within the prior support. It may take a long time to collect the remaining 10000 samples. Consider interrupting (Ctrl-C) and switching to 'sample_with_mcmc=True'.

This reason for this issue is described in more detail here and here. The following fixes are possible:

  • sample with MCMC: samples = posterior((num_samples,), x=x_o, sample_with_mcmc=True). This will make sampling slower, but samples will not ‘leak’.

  • resort to single-round SNPE and (if necessary) increase your simulation budget.

  • if your prior is either Gaussian (torch.distributions.multivariateNormal) or Uniform (sbi.utils.BoxUniform), you can avoid leakage by using a mixture density network as density estimator. I.e., using the flexible interface, set density_estimator='mdn'. When running inference, there should be a print statement “Using SNPE-C with non-atomic loss”

  • use a different algorithm, e.g. SNRE and SNLE. Note, however, that these algorithms can have different issues and potential pitfalls.