Notebooks about Bayesian methods for machine learning
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Updated
Mar 6, 2024 - Jupyter Notebook
Notebooks about Bayesian methods for machine learning
Python package for Bayesian Machine Learning with scikit-learn API
Code for "A-NICE-MC: Adversarial Training for MCMC"
This is a GitHub repository for our Bayeisan Machine Learning textbook, which includes the PDF for the book and accompanying Python notebooks.
This contains a number of IP[y]: Notebooks that hopefully give a light to areas of bayesian machine learning.
BayesianNonparametrics in julia
A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation
Key words: Bayesian analysis, Probabilistic programming, Data analysis, Bayesian machine learning... Using Python with its library PyMC3, pandas...
Exploration of TensorFlow-2 and TensorFlow probability to implement Bayesian Neural Networks, Normalizing flows, real NVPs and Autoencoders. Exploration of Bayesian Modelling and Variational Inference with Pyro.
Bayesian methods for machine learning course at CentraleSupélec
Bayesian Actor-Critic with Neural Networks. Developing an OpenAI Gym toolkit for Bayesian AC reinforcement learning.
Library for Bayesian machine learning
Repo of udemy bayesian machine learning A/B test
A Bayesian approach to predictive uncertainty in chemotherapy patients at risk of acute care utilization
Bayesian Machine Learning Project
This is a collection of algorithms and models written in Python for probabilistic programming. The main focus of the package is on Bayesian reasoning by using Bayesian networks, Markov networks, and their mixing.
Exercises on Bayesian linear regression, Gaussian Processes, Metropolis-Hastings Inference for Bayesian Logistic Regression, K-means and Probabilistic PCA. Made at Institut EURECOM (FR)
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