MIRA: A Score for Conditional Distribution Accuracy and Model Comparison
We introduce Mira, a sample-based score for assessing the accuracy of a candidate conditional distribution using only joint samples from the true data-generating process. Relying on the principle that distributions coincide if they assign equal probability mass to all regions, we derive an analytic expression for the Mira statistic, whose average defines the Mira score. This formulation further allows us to compute theoretical reference values and uncertainty estimates when the candidate distribution matches the true one. This framework enables model comparison by quantifying the alignment between the conditional distribution of a candidate model and the true data generating process. Cons...
| Authors | Sammy Sharief, Justine Zeghal, Gabriel Missael Barco et al. |
|---|---|
| Primary category | stat.ML |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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