Open question · OPEN-HEP-NUISANCE-SCALING

How do template fits scale with nuisance parameters?

Openpublication: open

Meaning

Understand systematic template morphing and the cost of larger nuisance models.

Statement

Develop scalable score/efficient-score construction for calibration, shape, normalization, MC-statistical, and correlated nuisance parameters.

Closest prior work

Nearest prior work

Alsing, Wandelt (2019)Nuisance Hardened Data Compression for Fast Likelihood-Free Inference

Relationships

Related settled results

Full-data efficient-score dominationDS-EFFICIENT-SCORE-DOMINATION
Linear-mixture component ratios suffice for score constructionMIXTURE-RATIO-SCORE

The full chain beneath it

Ds objective is the logdet of efficient/profiled Schur informationDS-SCHURdepth 2
Quantized Fisher-information identityFI-QUANT-IDENTITYdepth 2
Local score can be obtained from a local density ratioRATIO-LOCAL-SCOREdepth 2

Where it comes from

Raised by

Full-data efficient-score dominationDS-EFFICIENT-SCORE-DOMINATION
Linear-mixture component ratios suffice for score constructionMIXTURE-RATIO-SCORE

Local graph

Prerequisites to the left, what it enables or raises to the right, boundary cases below. Open in the map.

The question in full

Systematic template morphing and nuisance scalability

Score and efficient-score construction for calibration, template-shape, normalization, MC-statistical and correlated nuisance parameters without an impractical score dimension.

From OPEN_PROBLEMS.md