Bayesian inference with probabilistic programming.
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Updated
Jul 22, 2024 - Julia
Bayesian inference with probabilistic programming.
A Bayesian Analysis Toolkit in Julia
Probabilistic programming via source rewriting
Bayesian Statistics using Julia and Turing
Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.
Transformations to contrained variables from ℝⁿ.
Bayesian Generalized Linear models using `@formula` syntax.
Markov Chain Monte Carlo convergence diagnostics in Julia
Graphical tools for Bayesian inference and posterior predictive checks.
Metric Gaussian Variational Inference
Toolkit functions and example outputs for Bayesian (Structural) Vector Autoregressive (VAR) models
Bayesian Integration of functions
WIP successor to Soss.jl
Bayesian inference on wiring diagrams.
BayesBase is a package that serves as an umbrella, defining, exporting, and re-exporting methods essential for Bayesian statistics
Interface between Turing.jl and MonteCarloMeasurements.jl
A flexible group comparison test based on dependent Dirichlet process
A Julia Package for Bayesian Nonparametric Analysis for Machine Learning
Statistical analyses for Bayesian workflows
Bayesian A/B testing functions for test evaluations
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