A Bayesian approach to predictive uncertainty in chemotherapy patients at risk of acute care utilization
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Updated
Jun 4, 2023 - Python
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)
Numerical experiments to illustrate theoretical points of the article: A non-asymptotic analysis for stein variational gradient descent, Korba et al., 2021
The aim of this project is to apply Bayesian Machine Learning Algorithm to predict the diagnosis condition of the patients.
Assignment code for Bayesian ML course on Coursera
Platform for automatic processing of (aq-tngapms) Air Quality using TNGAPMS
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
Bayesian methods for machine learning course at CentraleSupélec
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.
Key words: Bayesian analysis, Probabilistic programming, Data analysis, Bayesian machine learning... Using Python with its library PyMC3, pandas...
A bare-bones TensorFlow framework for Bayesian deep learning and Gaussian process approximation
BayesianNonparametrics in julia
This contains a number of IP[y]: Notebooks that hopefully give a light to areas of bayesian machine learning.
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