The dataset accompanies the paper 'Truncated Marginal Neural Ratio Estimation' by Benjamin Kurt Miller from the University of Amsterdam. It is released under an Open Access (green) license. The dataset's specific size, format, and last update date are unknown.
Use Cases
- Benchmarking neural ratio estimation algorithms based on the described simulation framework.
- Training amortized inference networks using the generated simulation data.
- Evaluating the performance of truncated marginal methods for likelihood-free inference.
Strengths
- Dataset is directly linked to a peer-reviewed arXiv article, providing academic context.
- Associated software repository is referenced, suggesting potential for reproducibility.
Limitations
- Description metadata is limited; actual data quality requires manual inspection after download.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
Provenance
- Source
- University of Amsterdam