Bayesian Modeling and Probabilistic Programming in Python
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Updated
Sep 17, 2024 - Python
Bayesian Modeling and Probabilistic Programming in Python
Monte is a set of Monte Carlo methods in Python. The package is written to be flexible, clear to understand and encompass variety of Monte Carlo methods.
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
pocoMC: A Python implementation of Preconditioned Monte Carlo for accelerated Bayesian Computation
PyAutoFit: Classy Probabilistic Programming
A batteries-included toolkit for the GPU-accelerated OpenMM molecular simulation engine.
VIP is a python package/library for angular, reference star and spectral differential imaging for exoplanet/disk detection through high-contrast imaging.
Fast & scalable MCMC for all your exoplanet needs!
The Python ensemble sampling toolkit for affine-invariant MCMC
Python scripts and data sets that can be used to reproduce the results presented in the paper "Metropolis-Hastings with Scalable Subsampling".
Normalizing-flow enhanced sampling package for probabilistic inference in Jax
MCMC sample analysis, kernel densities, plotting, and GUI
Python tools for Bayesian data analysis
Autologistic Actor Attribute Model (ALAAM) parameter estimation, simulation, and goodness-of-fit
RADEX+emcee, LVG modeling with an MCMC approach, used in arXiv: 1709.04740
Use MCMC to analyze districting plans and gerrymanders
Nested Sampling post-processing and plotting
Active Learning Accelerated Bayesian Inference (ALABI)
Running Monte Carlo - Markov Chain algorithm on synthesized spectral models made by CLOUDY to compare them with data from CECILIA survey
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