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  • Installation
  • Getting started
  • PyVBMC: Frequently Asked Questions
  • Examples
    • PyVBMC Example 1: Basic usage
    • PyVBMC Example 2: Understanding the inputs and the output trace
    • PyVBMC Example 3: Output diagnostics and saving results
    • PyVBMC Example 4: Multiple runs as validation
    • PyVBMC Example 5: Prior distributions
    • PyVBMC Example 6: Noisy log-likelihood evaluations
    • PyVBMC Example 7: Stacking the posteriors of several runs (S-VBMC)
    • PyVBMC Example 8: Fitting a PyMC model
    • PyVBMC Example 9: Torch and JAX models and posterior exports
  • Documentation
    • VBMC
    • VariationalPosterior
      • VariationalPosterior.to_torch
      • VariationalPosterior.to_arviz
    • calibrate
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    • SVBMC
    • PyMCTarget
    • Advanced documentation
      • Classes
        • Acquisition Functions
        • CalibrationProfile
        • FunctionLogger
        • IterationHistory
        • Options
        • ParameterTransformer
        • PyMCTarget
        • SVBMC
        • Timer
        • VariationalPosterior
        • VBMC
      • Functions
        • active_sample
        • VBMC algorithm helpers
        • calibrate
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        • decorators
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        • get_hpd
        • kde_1d
        • whitening
      • VBMC options
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  • .rst

Classes

Classes#

  • Acquisition Functions
    • AbstractAcqFcn
    • AcqFcn
    • AcqFcnIMIQR
    • AcqFcnLog
    • AcqFcnNoisy
    • AcqFcnVIQR
    • AcqFcnVanilla
  • CalibrationProfile
    • CalibrationProfile
  • FunctionLogger
    • FunctionLogger
    • Batch evaluation
  • IterationHistory
    • IterationHistory
  • Options
    • Options
  • ParameterTransformer
    • ParameterTransformer
  • PyMCTarget
    • PyMC adapter at a glance
    • PyMC coordinate layout and hard bounds
    • PyMC setup and plausible-region accounting
    • Structured return to the PyMC model
    • PyMC model-scope limits
    • Persistence of adapted PyMC runs
  • SVBMC
    • Basic usage
    • ELBO reporting
    • A composite posterior
    • Helpers
    • Differences from the standalone svbmc package
    • Citation
  • Timer
    • Timer
  • VariationalPosterior
    • Performance settings
    • Export methods
  • VBMC
    • Reusing evaluations and charging initialization work
    • Performance calibration
    • Startup tips

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Acquisition Functions

© Copyright 2022, Machine and Human Intelligence research group (PI: Luigi Acerbi, University of Helsinki).

PyVBMC is one of the open-source model-fitting tools from the Machine and Human Intelligence Group at the University of Helsinki.