BlackJAX is a Bayesian Inference library designed for ease of use, speed and modularity.
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Updated
Aug 26, 2024 - Python
BlackJAX is a Bayesian Inference library designed for ease of use, speed and modularity.
Manifold Markov chain Monte Carlo methods in Python
PyTorch implementation of Bidirectional Monte Carlo, Annealed Importance Sampling, and Hamiltonian Monte Carlo.
Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods
PinNUTS🥜 is dynamic Hamiltonian Monte Carlo algorithm implemented in Python
The code enables to perform Bayesian inference in an efficient manner through the use of Hamiltonian Neural Networks (HNNs), Deep Neural Networks (DNNs), Neural ODEs, and Symplectic Neural Networks (SympNets) used with state-of-the-art sampling schemes like Hamiltonian Monte Carlo (HMC) and the No-U-Turn-Sampler (NUTS).
Official code for HH-VAEM
An experimental Python package for learning Bayesian Neural Network.
Hamiltonian Annealed Importance Sampling (HAIS) in tensorflow
Code implementing Integrator Snippets, joint work with Christophe Andrieu and Chang Zhang
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.
A Python package for likelihood-free inference (LFI) methods such as Approximate Bayesian Computation (ABC)
Statistics and Machine Learning in depth analysis with Tensorflow Probability
Bayesian Inference of open cluster ages from photometry, parallaxes and Lithium measurements.
This project contains the implementation of using the generative models in continuous authentication on motion sensor data from the H-MOG dataset.
This repo implements Robert, Wu, Stoehr, CP Robert - 2019 (https://arxiv.org/abs/1810.04449) algorithms eHMC and prHMC
Theano implementations of thermodynamic Monte Carlo algorithms
Using the Hamiltonian Monte Carlo algorithm, and a visualisation of how the algorithm works. 'MyMC3' - HMC is the main algorithm used in Bayesian statistical package PyMC3.
Implementation of folded Folded Hamiltonian Monte Carlo for data imputation and augmentation on data collected from cancer patients who self-reported their symptoms experience during chemotherapy by a team in the School of the Nursing University of California.
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