Displaying 1 to 20 from 51 results

PyMC3 - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

  •    Python

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning which focuses on advanced Markov chain Monte Carlo and variational fitting algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.Note: Running pip install pymc will install PyMC 2.3, not PyMC3, from PyPI.

stan - Stan development repository (home page is linked below)

  •    R

There are interfaces available in R, Python, MATLAB, Julia, Stata, Mathematica, and for the command line. There are separate repositories in the stan-dev GitHub organization for the interfaces, higher-level libraries and lower-level libraries.

pyro - Deep universal probabilistic programming with Python and PyTorch

  •    Python

Pyro was originally developed at Uber AI and is now actively maintained by community contributors, including a dedicated team at the Broad Institute. In 2019, Pyro became a project of the Linux Foundation, a neutral space for collaboration on open source software, open standards, open data, and open hardware. For more information about the high level motivation for Pyro, check out our launch blog post. For additional blog posts, check out work on experimental design and time-to-event modeling in Pyro.

classifier - [UNMAINTAINED] Bayesian classifier with Redis backend

  •    Javascript

Deprecation notice: This library is no longer actively maintained. Try the natural classifier. It doesn't have a Redis backend, but otherwise works even better. The first argument to train() can be a string of text or an array of words, the second argument can be any category name you want.

BDA_py_demos - Bayesian Data Analysis demos for Python

  •    Jupyter

to interactively run the IPython Notebooks in the browser. This repository contains some Python demos for the book Bayesian Data Analysis, 3rd ed by Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin (BDA3).

orbit - A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood

  •    Python

Orbit is a Python package for Bayesian time series forecasting and inference. It provides a familiar and intuitive initialize-fit-predict interface for time series tasks, while utilizing probabilistic programming languages under the hood.

Dclib - Portable C++ library

  •    C++

dlib is a library for developing portable applications dealing with networking, threads, graphical interfaces, data structures, linear algebra, machine learning, XML and text parsing, numerical optimization, Bayesian nets, data compression routines, linked lists, binary search trees, linear algebra and matrix utilities, machine learning algorithms, and many other general utilities.

Pyro - Deep universal probabilistic programming with Python and PyTorch

  •    Python

Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling.

bambi - BAyesian Model-Building Interface (BAMBI) in Python.

  •    Python

Bambi is a high-level Bayesian model-building interface written in Python. It's built on top of the PyMC3 probabilistic programming framework, and is designed to make it extremely easy to fit mixed-effects models common in social sciences settings using a Bayesian approach.Bambi requires a working Python interpreter (either 2.7+ or 3+). We recommend installing Python and key numerical libraries using the Anaconda Distribution, which has one-click installers available on all major platforms.

gelato - Bayesian dessert for Lasagne

  •    Python

Recent results in Bayesian statistics for constructing robust neural networks have proved that it is one of the best ways to deal with uncertainty, overfitting but still having good performance. Gelato will help to use bayes for neural networks. Library heavily relies on Theano, Lasagne and PyMC3.I use generic approach for decorating all Lasagne at once. Thus, for using Gelato you need to replace import statements for layers only. For constructing a network you need to be the in pm.Model context environment.

nipymc - Bayesian mixed-effects modeling of fMRI data in Python

  •    Shell

NiPyMC requires a working Python interpreter (either 2.7+ or 3+). We recommend installing Python and key numerical libraries using the Anaconda Distribution, which has one-click installers available on all major platforms.Once both packages are installed, you should be ready to fit models with NiPyMC.

beat - Bayesian Earthquake Analysis Tool

  •    Python

This repository is still beta version and under development! There might be future changes in the API that cause previous versions to break.An alternative: The geodetic data may be saved using the package "pickle" as a file "geodetic_data.pkl" containing a list of "GeodeticTarget", especially "CompoundGPS" or "DiffIFG" objects. Please see the heart.py module for specifics.

sampled - Decorator for PyMC3

  •    Python

Provides syntactic sugar for reusable models with PyMC3. This lets you separate creating a generative model from using the model.

naive-apl - Naive Bayesian Classifier written in APL

  •    APL

This is a simple naive bayesian classifier to gain independent probabilistic assumptions on test input. The classifier requires precisely 2 groups with training data. This is just a fun side project I did over the weekend, but any contributions would be fantastic.This example shows the classifier displaying the independent probabilistic assumptions on whether given text aligns with keywords of two different animals: cat or dog.

naive-bayes-classifier - yet another general purpose naive bayesian classifier.

  •    Python

yet another general purpose Naive Bayesian classifier. Note: Definitely you will need much more training data than the amount in the above example. Really, a few lines of text like in the example is out of the question to be sufficient training set.

bayesian-bandit.js - Bayesian bandit implementation for Node and the browser.

  •    Javascript

This is an adaptation of the Bayesian Bandit code from Probabilistic Programming and Bayesian Methods for Hackers, specifically d3bandits.js. The code has been rewritten to be more idiomatic and also usable as a browser script or npm package. Additionally, unit tests are included.

DynamicHMC.jl - Bare-bones implementation of robust dynamic Hamiltonian Monte Carlo methods.

  •    Julia

Bare-bones implementation of robust dynamic Hamiltonian Monte Carlo methods. This package implements a modern version of the “No-U-turn sampler” in the Julia language, mostly as described in Betancourt (2017), with some tweaks.

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