Welcome to bayesloop’s documentation!

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