Approximate variational inference in Julia
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Updated
Jun 15, 2024 - Julia
Approximate variational inference in Julia
Julia implementation of Ge et al's PRScs
Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.
Bayesian optimization for Julia
A common framework for implementing and using log densities for inference.
Julia package for automatically generating Bayesian inference algorithms through message passing on Forney-style factor graphs.
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…
Julia package for Bayesian joint latent class models of longitudinal and time-to-event models
Algorithms and case studies for the paper "Accelerating delayed-acceptance Markov chain Monte Carlo algorithms".
BayesianNonparametrics in julia
Julia implementation of some ABC algorithms.
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