API Reference
This section provides comprehensive documentation for all approxbayescomp classes, functions, and modules. The API documentation is automatically generated from the source code docstrings.
Overview
The approxbayescomp package is organized into several key modules:
- SMC - Sequential Monte Carlo ABC implementation
- Prior - Prior distribution classes
- Distance - Distance metrics for comparing observations
- Simulate - Data simulation functions
- Plotting - Visualization tools for ABC results
- Weighted Statistics - Weighted statistical functions
- Utilities - Helper functions and utilities
Quick Reference
Core Classes
| Class | Purpose | Module |
|---|---|---|
Model |
ABC model specification | approxbayescomp.smc |
Population |
ABC population container | approxbayescomp.smc |
Psi |
Tolerance schedule class | approxbayescomp.smc |
IndependentPrior |
Independent prior distributions | approxbayescomp.prior |
IndependentUniformPrior |
Independent uniform priors | approxbayescomp.prior |
Key Functions
| Function | Purpose | Module |
|---|---|---|
smc() |
Run SMC-ABC algorithm | approxbayescomp.smc |
compute_psi() |
Compute tolerance schedule | approxbayescomp.smc |
simulate_claim_data() |
Simulate claim data | approxbayescomp.simulate |
l1(), l2(), wasserstein() |
Distance metrics | approxbayescomp.distance |
plot_posteriors() |
Plot posterior distributions | approxbayescomp.plot |
weighted_quantile() |
Weighted quantiles | approxbayescomp.weighted |