A collection of Bayesian data analysis recipes using PyMC3
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Updated
Oct 9, 2023 - Jupyter Notebook
A collection of Bayesian data analysis recipes using PyMC3
Notebooks for the Practicals at the Deep Learning Indaba 2022.
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.
A colourful collection of codes and notebooks, like Planet Sakaar
Notebooks (mostly R but some PyMC3) covering Prof Richard McElreath's Statistical Rethinking 2 book (draft version up to 26th Sept 2019) and Homeworks from his winter 2019 lecture course
Repository covering basics of Bayesian inference
Simple Experiments mainly on Machine Learning
Notebooks for Advanced Statistical Inference(ASI) course at EURECOM
Notebooks
Some notebooks for learning about bayesian models
A collection of notebooks that demonstrate Bayesian data analysis
Collab Notebook Learned and Implemented During Spring 2024
Summary notebook implementing Bayesian Model Averaging with numpyro.
Jupyter Notebook solutions for Advanced Statistical Inference course at EURECOM
Presentation for part 1 and coding notebook for part 2 of my intro to Bayes sessions
Notebook for implementing Monte Carlo techniques (Metropolis-Hastings and Augmented Gibbs) to solve a Bayesian Probit regression.
This Jupyter Notebook implements Bayesian modeling techniques to fit a posterior distribution and forecast demand for an e-commerce company.
Notes about Statistical Rethinking | Richard McElreath
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