Theano implementations of thermodynamic Monte Carlo algorithms
-
Updated
Mar 27, 2017 - Python
Theano implementations of thermodynamic Monte Carlo algorithms
Used in Deep Machine Learning and Lattice Quantum Chromodynamics
Bayesian deep learning experiments
An experimental Python package for learning Bayesian Neural Network.
A lightweight and performant implementation of HMC and NUTS in Python, spun out of the PyMC project.
How Bayesian should Bayesian Optimisation be?
R package that uses rstan capabilities to sample using `HMC' and inference for developmental rate dynamics of species in ecology
Modified TensorFlow implementation for training MCMC samplers on Lattice Gauge Theory models from the paper: Generalizing Hamiltonian Monte Carlo with Neural Network
A dashboard build with bokeh to provide interative data visualization and exploration of survey results.
Hamiltonian Monte Carlo (HMC) sampling method in Python3, based on the original paper: Simon Duane, Anthony D. Kennedy, Brian J. Pendleton and Duncan Roweth (1987). "Hybrid Monte Carlo". Physics Letters B. 195 (2): 216–222.
Code accompanying the paper 'Manifold MCMC methods for Bayesian inference in a wide class of diffusion models'
Add a description, image, and links to the hmc topic page so that developers can more easily learn about it.
To associate your repository with the hmc topic, visit your repo's landing page and select "manage topics."