Bayesian inference with probabilistic programming.

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Types and utility functions for summarizing Markov chain Monte Carlo simulations

Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms

Implementation of normalising flows and constrained random variable transformations

Implementation of domain-specific language (DSL) for dynamic probabilistic programming

Automatic differentiation of Distributions using Tracker, Zygote, ForwardDiff and ReverseDiff

Robust implementation for random-walk Metropolis-Hastings algorithms

Abstract types and interfaces for Markov chain Monte Carlo methods

Implementation of variational Bayes inference algorithms

Bayesian Generalized Linear models using `@formula` syntax.

Implementation of advanced Sequential Monte Carlo and particle MCMC algorithms

Implementations of single and multi-ellipsoid nested sampling

Implementations of parallel tempering algorithms to augment samplers with tempering capabilities

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Common types and interfaces for probabilistic programming

Implementation of domain specific language (DSL) for probabilistic graphical models

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An implementation of PSIS algorithms in Julia.

Tape based task copying in Turing

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Julia implementation of elliptical slice sampling.

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Slice sampling algorithms in Julia

MCMCDebugging.jl: debugging utilities for MCMC samplers

Common abstractions for state-space models