PinNUTS🥜 is dynamic Hamiltonian Monte Carlo algorithm implemented in Python
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Updated
Feb 7, 2020 - Python
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).
An efficient Python implementation for Bayesian inference in binary stars based on Stan.
JAX-powered Hi-Fi mocks
Package to do Bayesian inference with Gibbs sampler
Bayesian Conditional Transformation Models by Manuel Carlan, Thomas Kneib and Nadja Klein
Bayesian inference using the No-U-Turn sampler.
Java and Processing implementations for visualising various MCMC methods.
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