Bayesian inference with probabilistic programming.
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Updated
Jul 30, 2024 - Julia
Bayesian inference with probabilistic programming.
Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms
BlackJAX is a Bayesian Inference library designed for ease of use, speed and modularity.
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.
JAX-powered Hi-Fi mocks
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.
Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python
Statistics and Machine Learning in depth analysis with Tensorflow Probability
Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.
Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods
Manifold Markov chain Monte Carlo methods in Python
Delayed Rejection Generalized HMC sampler
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