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"](https://arxiv.org/abs/2110.06581) 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: ```bibtex @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](https://huggingface.co/datasets/k-kiirikki/trust-dvine-4d) - 4-dimensional DVine results数据集： - k-kiirikki/trust-dvine-6d 注释创建者： - 无注释 语言： - 英语 许可协议：Apache-2.0 多语言属性： - 单语言 规模类别： - 样本量少于1000 源数据集： - 原创数据集 任务类别： - 其他 # Trust-DVine-6D：基于模拟的推断基准测试集（6维） 本数据集包含来自论文《Averting A Crisis in Simulation-Based Inference》（链接：https://arxiv.org/abs/2110.06581）中的**6维DVine（Dependency Vine，依赖藤）**问题的基于模拟的推断（Simulation-Based Inference, SBI）结果。 ## 数据集说明 DVine（依赖藤）问题通过基于藤柯普利（vine copula）的模拟器，在6维参数空间上评估贝叶斯推断方法。本数据集包含以下内容： ### 参考数据（`vine_reference/`） - `coverage-vine-posterior.npy`：藤后验分布的覆盖统计量 - `coverage-vine-mst.npy`：最小生成树（Minimum Spanning Tree, MST）藤的覆盖统计量 - `sbc-vine-posterior.npy`：藤后验分布的基于模拟的校准（Simulation-Based Calibration, SBC）结果 - `sbc-vine-mst.npy`：最小生成树藤的SBC结果 ### 模拟结果（`output/{样本量}/`） 结果按模拟次数（样本量：1024、2048、4096、8192、16384、32768、65536、131072）进行组织。 每个样本量目录下包含无正则化场景（`without-regularization/`）下多种方法的结果： - **MLP（多层感知机，Multi-Layer Perceptron）方法**：`mlp-{00-04}`、`mlp-bagging-{00-04}`、`mlp-static-{00-04}` - **Flow-SBI（流模型驱动的模拟推断方法）**：`flow-sbi-{00-04}`、`flow-sbi-bagging-{00-04}`、`flow-sbi-static-{00-04}` ### 文件类型 - `coverage.npy`：覆盖诊断数组 - `sbc.npy`：基于模拟的校准数组 - `diagnostic.npy`：额外诊断指标 - `losses-*.npy`：训练/验证/测试损失 - `test-loss-functionals.npy`：测试损失泛函 - `posterior.pkl`：序列化后验样本 ## 引用说明 若使用本数据集，请引用以下文献： bibtex @article{terral2022averting, title={Averting A Crisis in Simulation-Based Inference}, author={Terral, Thomas and others}, journal={arXiv preprint arXiv:2110.06581}, year={2022} } ## 相关数据集 - [k-kiirikki/trust-dvine-4d](https://huggingface.co/datasets/k-kiirikki/trust-dvine-4d)：4维DVine（依赖藤）问题的推断结果