78 Open source projects that are alternatives of or similar to Emcee

Bayesian Generalized Linear Models with Time-Varying Coefficients

Equilibrium Expectation for ERGM parameter estimation for large directed networks

A multi-planet Radial Velocity and Transit modelling software

A common framework for implementing and using log densities for inference.

Collection of Monte Carlo (MC) and Markov Chain Monte Carlo (MCMC) algorithms applied on simple examples.

Implementing MCMC sampling from scratch in R for various Bayesian models

Bayesian nowcasting with adjustment for delayed and incomplete reporting to estimate COVID-19 infections in the United States

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara

Single-cell copy number calling and event history reconstruction.

PhyML -- Phylogenetic estimation using (Maximum) Likelihood

Examples for Bayesian inference using DynamicHMC.jl and related packages.

Manifold Markov chain Monte Carlo methods in Python

Documentation, models and code relating to the 3rd edition of the textbook Stochastic Modelling for Systems Biology

Regression with Summary Statistics.

Boltzmann Machines in TensorFlow with examples

Latent Dirichlet Allocation

library of C++ functions that support applications of Stan in Pharmacometrics

A web interface for exploring PyMC3 traces

Make a sweet giant triangle confusogram (GTC) plot

An R package to manipulate MCMC samples

Sequential Monte Carlo algorithm for approximation of posterior distributions.

Bayesian Deep Learning with Stochastic Gradient MCMC Methods

Doing Bayesian Data Analysis, 2nd Edition (Kruschke, 2015): Python/PyMC3 code

DGMs for NLP. A roadmap.

Application of the L2HMC algorithm to simulations in lattice QCD.

BAS R package https://merliseclyde.github.io/BAS/

Stan.jl illustrates the usage of the 'single method' packages, e.g. StanSample, StanOptimize, etc.

bayesplot R package for plotting Bayesian models

CorBinian: A toolbox for modelling and simulating high-dimensional binary and count-data with correlations

Bayesian Data Analysis demos for Python

Multi-core BUGS for fast Bayesian inference of large hierarchical models

Automatic Reparameterisation of Probabilistic Programs

Nested Sampling post-processing and plotting

Bayesian methods in deep learning Summer School

Markov chain Monte Carlo solver for lattice spin systems implemented by Julialang

Python MCMC Sampler

machine learning

RStan, the R interface to Stan

A statistical toolbox for diffusion processes and stochastic differential equations. Named after the Brownian Bridge.

Full Bayesian Inference for Hidden Markov Models

Blang's software development kit

CmdStan.jl v6 provides an alternative, older Julia wrapper to Stan's `cmdstan` executable.

DynamicHMC versions of StatisticalRethinking models

CmdStanR: the R interface to CmdStan

The sample question for Interview a job in Binary options

Bitmap generation from a single example with convolutions and MCMC

Using neural network to build expressive hierarchical distribution; A variational method to accurately estimate posterior uncertainty; A fast and general method for Bayesian inference. (ICML 2018)

Start with the Chempy tutorial

Julia version of selected functions in the R package `rethinking`. Used in the StatisticalRethinkingStan and StatisticalRethinkingTuring projects.

Owl - OCaml Scientific and Engineering Computing @ http://ocaml.xyz

Collection of probabilistic models and inference algorithms

A C++ library of Markov Chain Monte Carlo (MCMC) methods

shinystan R package and ShinyStan GUI

Bayesian Data Analysis demos for R

Material for a workshop on Bayesian stats with R

Distributed Bayesian Entity Resolution in Apache Spark

AAAI & CVPR 2016: Preconditioned Stochastic Gradient Langevin Dynamics (pSGLD)

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

High-performance Bayesian Data Analysis on the GPU in Clojure

Pieces of code that have appeared on my blog with a focus on stochastic simulations.