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PyVBMC - Home PyVBMC - Home
  • 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
    • VBMC options
    • Priors
    • SVBMC
    • PyMCTarget
    • Advanced documentation
      • Classes
        • Acquisition Functions
        • CalibrationProfile
        • FunctionLogger
        • IterationHistory
        • Options
        • ParameterTransformer
        • PyMCTarget
        • SVBMC
        • Timer
        • VariationalPosterior
        • VBMC
      • Functions
        • active_sample
        • VBMC algorithm helpers
        • calibrate
        • check_for_updates
        • create_vbmc_animation
        • decorators
        • Entropy
        • get_hpd
        • kde_1d
        • whitening
      • VBMC options
  • Instructions for developers and contributors
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  • .rst

Functions

Functions#

Functions:

  • active_sample
  • VBMC algorithm helpers
  • calibrate
  • check_for_updates
  • create_vbmc_animation
  • decorators
  • Entropy
  • get_hpd
  • kde_1d
  • whitening

previous

Timer

next

active_sample

© 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.