How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
The Python ensemble sampling toolkit for affine-invariant MCMC
The main features of dfm/emcee are: Probabilistic Modeling, Statistical Modeling.
Open-source alternatives to dfm/emcee include: pymc-devs/pymc — PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian… ctgk/prml — PRML is a Python machine learning library and statistical learning toolkit. It provides code implementations of… arviz-devs/arviz — Exploratory analysis of Bayesian models with Python. blei-lab/edward — Edward is a probabilistic programming language and inference engine designed for building deep generative models and… camdavidsonpilon/probabilistic-programming-and-bayesian-methods-for-hackers — This project is a computational statistics textbook and Bayesian data analysis course. It serves as a guide for… bambinos/bambi — BAyesian Model-Building Interface (Bambi) in Python.
PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian inference. It provides a probabilistic graphical model library for specifying random variables, priors, and likelihood functions, supported by an MCMC sampling engine and variational inference tools to estimate posterior distributions. The framework features a GPU-accelerated inference backend that compiles models into machine code to increase execution speed. It utilizes a backend-agnostic tensor execution model and just-in-time graph compilation to optimize the computation o
PRML is a Python machine learning library and statistical learning toolkit. It provides code implementations of supervised and unsupervised learning concepts, including regression, classification, and neural network algorithms for statistical data modeling. The project functions as a pattern recognition toolkit used to identify theoretical structures within numerical datasets. It includes a neural network framework for solving nonlinear data mappings and a linear algebra toolkit that utilizes vectorized operations and matrix calculations. The library covers a broad range of capabilities, inc
Exploratory analysis of Bayesian models with Python
BAyesian Model-Building Interface (Bambi) in Python.