Datasets:

k-kiirikki
/

trust-dvine-6d

datasets:
  - k-kiirikki/trust-dvine-6d
annotations_creators:
  - no-annotation
language:
  - en
license: apache-2.0
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - original
task_categories:
  - other

Trust-DVine-6D: Simulation-Based Inference Benchmark (6D)

This dataset contains simulation-based inference results for the 6-dimensional DVine problem from the "Averting A Crisis in Simulation-Based Inference" paper.

Dataset Description

The DVine (Dependency Vine) problem evaluates Bayesian inference methods on a 6-dimensional parameter space using a vine copula-based simulator. This dataset contains:

Reference Data (`vine_reference/`)

coverage-vine-posterior.npy - Coverage statistics for the vine posterior
coverage-vine-mst.npy - Coverage statistics for the MST (Minimum Spanning Tree) vine
sbc-vine-posterior.npy - Simulation-based calibration results for vine posterior
sbc-vine-mst.npy - SBC results for MST vine

Simulation Results (`output/{sample_size}/`)

Results organized by number of simulations (sample sizes: 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072).

Each sample size directory contains results from multiple methods (without-regularization/):

MLP methods: mlp-{00-04}, mlp-bagging-{00-04}, mlp-static-{00-04}
Flow-SBI methods: flow-sbi-{00-04}, flow-sbi-bagging-{00-04}, flow-sbi-static-{00-04}

File Types

coverage.npy - Coverage diagnostic arrays
sbc.npy - Simulation-based calibration arrays
diagnostic.npy - Additional diagnostic metrics
losses-*.npy - Training/validation/test losses
test-loss-functionals.npy - Test loss functionals
posterior.pkl - Serialized posterior samples

Citation

If you use this dataset, please cite:

@article{terral2022averting,
  title={Averting A Crisis in Simulation-Based Inference},
  author={Terral, Thomas and others},
  journal={arXiv preprint arXiv:2110.06581},
  year={2022}
}

Related Datasets

k-kiirikki/trust-dvine-4d - 4-dimensional DVine results