Bayesian inference with probabilistic programming.
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Updated
Jul 16, 2024 - Julia
Bayesian inference with probabilistic programming.
Julia package with selected functions in the R package `rethinking`. Used in the SR2... projects.
Types and utility functions for summarizing Markov chain Monte Carlo simulations
A Bayesian Analysis Toolkit in Julia
Implementations of the models from the Statistical Rethinking book with Turing.jl
StatisticalRethinking notebook project using Turing and Pluto notebooks (derived from Max Lapan's Jupyter project)
Robust implementation for random-walk Metropolis-Hastings algorithms
Distributed and parallel sampling from intractable distributions
Simulation, visualization, and inference of individual level infectious disease models with Julia
Bayesian Generalized Linear models using `@formula` syntax.
Sequential Monte Carlo algorithm for approximation of posterior distributions.
Markov chain Monte Carlo solver for lattice spin systems implemented in Julialang
A common framework for implementing and using log densities for inference.
Comparing performance and results of mcmc options using Julia
Affine Invariant Markov Chain Monte Carlo (MCMC) Ensemble sampler
Implementations of parallel tempering algorithms to augment samplers with tempering capabilities
MCMC Inference for a Hawkes process in Julia
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