PyTorch implementation of Continuously Indexed Flows paper, with many baseline normalising flows

Aesara is a Python library for defining, optimizing, and efficiently evaluating mathematical expressions involving multi-dimensional arrays.

NFMC benchmark and code for the paper: "Nabergoj and Štrumbelj. Empirical evaluation of normalizing flows in Markov Chain Monte Carlo, 2024."

This package implements various flow-based MCMC algorithms for statistical analyses and sampling.

Python library for analysis of time series data including dimensionality reduction, clustering, and Markov model estimation

🧙‍♂️ ImageMagick 7

Flow Annealed Importance Sampling Bootstrap (FAB). ICLR 2023.

JAX - A curated list of resources https://github.com/google/jax

Flax is a neural network library for JAX that is designed for flexibility.

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

Large-Scale Multimodal Dataset of Astronomical Data

PyTorch and MATLAB implementations of tensorizing-flow for variational inference, focusing on (1) high-dimensional sample generation using tensor-network representation of probability densities. (2…

Differentiable, Hardware Accelerated, Molecular Dynamics

BlackJAX is a Bayesian Inference library designed for ease of use, speed and modularity.

Fast data visualization and GUI tools for scientific / engineering applications

Differentiable Cosmological Forward Model

Code to reproduce experiments in Transport Elliptical Slice Sampling

PyTorch implementation of the OT-Flow approach in arXiv:2006.00104

MCHMC: sampler from an arbitrary differentiable distribution

nessai: Nested Sampling with Artificial Intelligence

Potential functions for sampling or optimization

Modern normalizing flows in Python. Simple to use and easily extensible.

Bootplot is a package for black-box uncertainty visualization.

pocoMC: A Python implementation of Preconditioned Monte Carlo for accelerated Bayesian Computation