bayesian-data-analysis

Bayesian Data Analysis demos for Python

RStan, the R interface to Stan

Python/PyMC3 port of the examples in " Statistical Rethinking A Bayesian Course with Examples in R and Stan" by Richard McElreath

Bayesian analysis + tidy data + geoms (R package)

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

How to do Bayesian statistical modelling using numpy and PyMC3

A collection of Bayesian data analysis recipes using PyMC3

Bayesian Data Analysis demos for R

High-performance Bayesian Data Analysis on the GPU in Clojure

rstanarm R package for Bayesian applied regression modeling

shinystan R package and ShinyStan GUI

Statistical Rethinking with PyTorch and Pyro

loo R package for approximate leave-one-out cross-validation (LOO-CV) and Pareto smoothed importance sampling (PSIS)

Graphical tools for analyzing Markov Chain Monte Carlo simulations from Bayesian inference

Example PyMC3 project for performing Bayesian data analysis using a probabilistic programming approach to machine learning.

A probabilistic programming language that combines automatic differentiation, automatic marginalization, and automatic conditioning within Monte Carlo methods.

Doing Bayesian statistics in Python!

🚫 ↩️ A document that introduces Bayesian data analysis.

Solutions of practice problems from the Richard McElreath's "Statistical Rethinking" book.

'Visualization in Bayesian workflow' by Gabry, Simpson, Vehtari, Betancourt, and Gelman. (JRSS discussion paper and code)

Bayesian Data Analysis demos for Matlab/Octave

Stanマニュアルの日本語への翻訳プロジェクト

Reproducing plots of Bayesian Data Analysis (Gelman et al, 3rd Edition) in Python

Solutions and workflow for the Bayesian Statistics The Fun Way book in Python

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

Tools for Developing R Packages Interfacing with Stan

Bayesian multilevel mediation models in R

Pure julia implementation of Multiple Affine Invariant Sampling for efficient Approximate Bayesian Computation

Bayesian Cost Effectiveness Analysis. Given the results of a Bayesian model (possibly based on MCMC) in the form of simulations from the posterior distributions of suitable variables of costs and clinical benefits for two or more interventions, produces a health economic evaluation. Compares one of the interventions (the "reference") to the others ("comparators"). Produces many summary and plots to analyse the results