Displaying 1 to 13 from 13 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.

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.

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-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.


  •    Python

This package uses a Bayesian formulation and Markov chain Monte Carlo sampling methods to derive posterior distributions of subsurface and measured data properties. The current implementation is applied to time and frequency domain electro-magnetic data. Application outside of these data types is well within scope. Currently there are two types of data that we have implemented; frequency domain electromagnetic data, and time domain electromagnetic data. The package comes with a frequency domain forward modeller, but it does not come with a time domain forward modeller. See the section Installing the time domain forward modeller for more information.

Dropout_BBalpha - Implementations of the ICML 2017 paper (with Yarin Gal)

  •    Python

Please consider citing the paper when any of the material is used for your research. Contributions: Yarin wrote most of the functions in BBalpha_dropout.py, and Yingzhen (me) derived the loss function and implemented the adversarial attack experiments.

go-topics - Latent Dirichlet Allocation

  •    Go

A very basic LDA (Latent Dirichlet Allocation) implementation with some convenient utilities.