PARIS: Parallel Adaptive Reweighting Importance Sampling

PARIS is an efficient adaptive importance sampler designed for high-dimensional, multi-modal Bayesian inference. It combines global exploration with local adaptation to tackle complex posteriors in astrophysics and beyond.

Installation

pip install parismc

Getting Started

import numpy as np
from parismc import Sampler, SamplerConfig

# 1. Define log-density
def log_density(x):
    return -0.5 * np.sum(x**2, axis=1)

# 2. Configure & Initialize
config = SamplerConfig(alpha=1000)
sampler = Sampler(ndim=2, n_seed=5, log_density_func=log_density,
                  init_cov_list=[np.eye(2)*0.1]*5, config=config)

# 3. Run
sampler.prepare_lhs_samples(1000, 100)
sampler.run_sampling(500, './results')

Contents

Documentation

• User Guide
  • Configuration & Tuning
  • Using Prior Transforms
  • Advanced Usage
  • Custom Initialization
• API Reference
  • Core Classes
  • Utilities

Tutorials

• PARIS Tutorials

General

• General Information

Indices and tables

• Index

• Search Page