Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.
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Updated
Nov 30, 2023 - Julia
Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.
Julia package for automatically generating Bayesian inference algorithms through message passing on Forney-style factor graphs.
Bayesian optimization for Julia
A common framework for implementing and using log densities for inference.
BayesianNonparametrics in julia
Markov Chain Monte Carlo convergence diagnostics in Julia
Bayesian optimization is a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, wh…
Approximate variational inference in Julia
Algorithms and case studies for the paper "Accelerating delayed-acceptance Markov chain Monte Carlo algorithms".
Julia package for Bayesian joint latent class models of longitudinal and time-to-event models
Julia implementation of some ABC algorithms.
Julia implementation of Ge et al's PRScs
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