Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Conditional DR-ABC (conditional embedding operator summaries) instead of SA-ABC | ABC summary statistic construction · Toy Gaussian hierarchical model | MSE of theta: conditional DR-ABC does this consistently by a large margin | Helped |
| Full DR-ABC instead of K2-ABC | ABC summary statistic construction · Toy Gaussian hierarchical model | MSE of theta: outperforming K2-ABC by a large margin | Helped |
| Conditional DR-ABC instead of K2-ABC | ABC summary statistic construction · Ecological blowfly dynamics | MSE of theta: conditional DR-ABC outperforms K2-ABC in all test situations | Helped |
| Full DR-ABC instead of K2-ABC | ABC summary statistic construction · Ecological blowfly dynamics | MSE of theta | Mixed |
| DR-ABC methods instead of SA-ABC | ABC summary statistic construction · Ecological blowfly dynamics | MSE of theta: outperform SA-ABC by a large margin across the whole spectrum | Helped |
| Conditional DR-ABC instead of full DR-ABC | ABC summary statistic construction · Lotka-Volterra model | MSE of theta | Mixed |
| DR-ABC methods instead of K2-ABC and SA-ABC | ABC summary statistic construction · Lotka-Volterra model | MSE of theta: outperforms competing methods by a large margin; SA-ABC errors 1-2 orders of magnitude larger | Helped |
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