Bayesian inference with probabilistic programming.
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Updated
May 24, 2024 - Julia
Bayesian inference with probabilistic programming.
BlackJAX is a Bayesian Inference library designed for ease of use, speed and modularity.
JAX-powered Hi-Fi mocks
Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms
Code implementing Integrator Snippets, joint work with Christophe Andrieu and Chang Zhang
Survival analysis in health economic evaluation using Bayesian modelling and Hamiltonian Monte Carlo Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation.
Novel Markov chain Monte Carlo algorithm for sampling from multi-scale distributions
Bayesian Inference of open cluster ages from photometry, parallaxes and Lithium measurements.
A C++ library of Markov Chain Monte Carlo (MCMC) methods
Application of the L2HMC algorithm to simulations in lattice QCD.
Statistics and Machine Learning in depth analysis with Tensorflow Probability
Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.
Survival analysis in health economic evaluation Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package flexsurv) and a Bayesian perspective.
Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods
Manifold Markov chain Monte Carlo methods in Python
Delayed Rejection Generalized HMC sampler
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.
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