Welcome to JMCTF!

Python tools for perfoming Monte-Carlo studies of joint distribution functions consisting of many independent components, powered by TensorFlow.

Contents:

• Installation
• Quick Start
  • Fit MLEs to samples under a hypothesis (or many hypotheses)
  • Build and analyse test statistics
• Basic usage
  • Combine independent “analyses” into a joint distribution
  • Sample from the joint distribution
  • Find MLEs and conduct simple likelihood ratio tests
    • Likelihood ratio tests
    • Maximum likelihood estimators
• Advanced usage
  • Extending JMCTF with new analysis objects
• Look-elsewhere effect
  • Background theory and motivation
  • A simple example of a look-elsewhere effect correction
• Examples from quick start
• jmctf package
  • Module contents
  • Analysis classes
    • NormalAnalysis
    • BinnedAnalysis
  • JointDistribution
  • jmctf.common module

Indices and tables

• Index
• Module Index
• Search Page