Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
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Updated
Oct 20, 2023 - Jupyter Notebook
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
Bayesian inference with probabilistic programming.
tmLQCD is a freely available software suite providing a set of tools to be used in lattice QCD simulations. This is mainly a HMC implementation (including PHMC and RHMC) for Wilson, Wilson Clover and Wilson twisted mass fermions and inverter for different versions of the Dirac operator. The code is fully parallelised and ships with optimisations…
A C++ library of Markov Chain Monte Carlo (MCMC) methods
Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms
Manifold Markov chain Monte Carlo methods in Python
A native Julia code for lattice QCD with dynamical fermions in 4 dimension.
AeMCMC is a Python library that automates the construction of samplers for Aesara graphs representing statistical models.
An Ansible collection for the IBM Z HMC
A Prometheus exporter for the IBM Z HMC
Application of the L2HMC algorithm to simulations in lattice QCD.
A lightweight and performant implementation of HMC and NUTS in Python, spun out of the PyMC project.
Bayesian Generalized Linear models using `@formula` syntax.
Utilities of gauge fields
Hybrid Memory Cube Simulation & Research Infrastructure
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