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FlatPPL for Python
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FlatPPL for Python

Get started

  • Installation

Tutorials

  • Compile your first function
  • Worked tutorials
    • Sample a posterior with BlackJAX NUTS
    • Sample and approximate a posterior with BlackJAX
    • Sample a posterior with NumPyro’s NUTS
    • Fit maximum a posteriori and maximum likelihood points with Optax
    • Diagnose draws with ArviZ
    • Sample a posterior with nutpie
    • Fit with optimistix BFGS and Levenberg-Marquardt
    • Fit with iminuit and read Hesse and Minos errors
    • Fit a mixed model with automatic coordinate transforms
    • From a model file and a dataframe to checked posterior draws
    • A published Belle II likelihood: pyhf to FlatPPL to JAX
    • Fit a Gaussian regression with runtime data
    • Save a regression, reload it, and fit MAP
    • Read histograms from ROOT files with uproot and hist
    • A discovery fit from ROOT histograms with cabinetry
    • Export a compiled density with jax.export
    • Evaluate batched toys on CPU and GPU
    • Query any binding of a model
    • Inspect a model’s graph
    • Check a model from the example corpus
  • Import a pyhf model

Reference

  • Coordinate transforms for inference
  • Log densities for inference packages
  • Modules and contexts
  • Python values and signatures
  • Regression formulas
  • Black boxes
  • JAX and differentiation
  • Numerical integration
  • API reference
  • Example corpus qualification
  • Development and documentation
  • Changelog
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