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Fix link in tutorial
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michaeldeistler committed Nov 30, 2023
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4 changes: 2 additions & 2 deletions tutorials/13_diagnostics_simulation_based_calibration.ipynb
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"source": [
"# Simulation-based Calibration in SBI\n",
"\n",
"After a density estimator has been trained with simulated data to obtain a posterior, the estimator should be made subject to several diagnostic tests, before being used for inference given the actual observed data. *Posterior Predictive Checks* (see tutorial 12) provide one way to \"critique\" a trained estimator via its predictive performance. Another important approach to such diagnostics is simulation-based calibration as reported by [Talts et al, 2018](https://arxiv.org/abs/1804.06788).\n",
"After a density estimator has been trained with simulated data to obtain a posterior, the estimator should be made subject to several diagnostic tests, before being used for inference given the actual observed data. *Posterior Predictive Checks* (see [previous tutorial](https://sbi-dev.github.io/sbi/tutorial/12_diagnostics_posterior_predictive_check/)) provide one way to \"critique\" a trained estimator via its predictive performance. Another important approach to such diagnostics is simulation-based calibration as developed by [Cook et al, 2006](https://www.tandfonline.com/doi/abs/10.1198/106186006X136976) and [Talts et al, 2018](https://arxiv.org/abs/1804.06788).\n",
"\n",
"**Simulation-based calibration** (SBC) provides a (qualitative) view and a quantitive measure to check, whether the uncertainties of the posterior are balanced, i.e., neither over-confident nor under-confident. As such, SBC can be viewed as a necessary condition (but not sufficient) for a valid inference algorithm: If SBC checks fail, this tells you that your inference is invalid. If SBC checks pass, this is no guarantee that the posterior estimation is working."
]
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
"version": "3.10.11"
},
"name": "13_diagnosis_sbc.ipynb"
},
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