Research map / Probabilistic and scientific modeling / Variational and generative sampling
Automatic Posterior Transformation for Likelihood-Free Inference
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use APT with a mixture-density network instead of SNPE-A, SNPE-B, or SNL | two-moons · two-moons | posterior quality | Helped |
| Use flow-based APT instead of SNPE-A or SNPE-B | SLCP · SLCP | maximum mean discrepancy | Helped |
| Append uninformative data dimensions | APT · SLCP with uninformative observations | maximum mean discrepancy | Hurt |
| Append uninformative data dimensions | SNL · SLCP with uninformative observations | maximum mean discrepancy | Hurt |
| Use APT instead of other methods | Lotka-Volterra · Lotka-Volterra summary statistics | simulation efficiency | Helped |
| Use an RNN to map raw time-series directly to the posterior instead of summary statistics | APT · Lotka-Volterra raw time-series | posterior concentration around true parameters | No clear effect |
| Use an RNN on raw time-series instead of summary statistics | APT · Lotka-Volterra with observation noise | posterior quality | Helped |
| Use a CNN with APT on image observations | rock-paper-scissors reaction-diffusion model · RPS images | negative log-probability of ground-truth parameters | Helped |
| Use APT instead of SNPE-A or SNPE-B | M/G/1 · M/G/1 | posterior accuracy | Helped |
| Use APT instead of SNPE-A | GLM · GLM | posterior accuracy | Helped |
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