Kitchen Sink

• Introduction to Spey: A Unified Statistical Inference Framework
  • Motivation
  • The Plugin Architecture
  • Statistical Framework
  • Experimental Data: CMS-SUS-20-004
  • Simplified Likelihood: Correlated Background Model
  • Third Moment Expansion
  • Effective Sigma
  • Comparing the Three Likelihood Prescriptions
• Inference with parameter dependent signal yields
  • Including uncertainties
  • Combining likelihoods
• Statistical inference on Histogram
• Reinterpretation of Cross-Section Limits with Spey
  • 1. Read and Parse the CSV Data
  • 2. Fit Interpolating Functions
  • 3. Validation Plot — Reproducing Figure 12
  • 4. Spey Statistical Model Construction
  • 5. Exclusion \(CL_s\) Evaluation
• Gradient of a Statistical Model
• Signal Uncertainties
  • Introduction
  • Likelihood structure without signal uncertainties
  • How signal uncertainties modify the likelihood
  • Which backends accept signal uncertainties
  • Baseline model
  • Adding signal uncertainties
  • Visualising the effect on the \(\chi^{2}\) profile
  • Effect on exclusion limits
  • Summary
• Multi-Parameter Confidence Contours with Spey
  • Contents
  • 1 · Theoretical Background
  • 2 · Experimental Setup
  • 3 · Building the Statistical Model
  • 4 · Exploring the Likelihood Landscape
  • 5 · Running find_contour
  • 6 · Visualising the Confidence Contour
  • 7 · Algorithm Walk-through
  • Summary
• Building a Plug-in
  • Neutrino Experiment (example)
  • Simple Poisson (example)
• Introduction to Spey (PyHEP 2023)