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Welcome to bayesloop’s documentation!¶

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Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

See also

If you want to contribute to the project or just browse the source code, visit the Github repository of bayesloop.

Contents¶

  • Installation
    • Development version
    • Dependencies
    • Optional dependencies
  • Tutorials
    • First steps with bayesloop
      • Study class
      • Data import
      • Observation model
      • Transition model
      • Model fit
      • Plotting
      • Saving studies
    • Model Selection
      • Bayes factors
      • Combined transition models
      • Serial transition model
    • Optimization of hyper-parameters
      • Global optimization
      • Conditional optimization in nested transition models
    • Hyper-study
    • Change-point study
      • Analyzing abrupt changes of parameter values
      • Analyzing structural breaks in time series models
    • Online study
    • Prior distributions
      • Parameter prior
      • Hyper-parameter priors
    • Custom observation models
      • Sympy.stats random variables
      • Scipy.stats probability distributions
      • NumPy likelihood functions
    • Probability parser
    • Multiprocessing
  • Examples
    • Anomalous diffusion
      • Diffusion gradient
      • Regime-switch diffusion process
    • Stock market fluctuations
      • Persistent random walk model
      • Online study
      • Volatility spikes
      • Islands of stability
      • Automatic tuning
      • Real-time model selection
  • API Reference
    • Study types
    • Observation models
    • Transition models
    • File I/O
    • Probability Parser

Indices and tables¶

  • Index
  • Module Index
  • Search Page
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© Copyright 2016-2017, Christoph Mark. Revision 9fa73c0d.

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