Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
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Bayesian Modelling In Python | 2,202 | 6 years ago | Jupyter Notebook | |||||||
A python tutorial on bayesian modeling techniques (PyMC3) | ||||||||||
Bayesian Stats Modelling Tutorial | 601 | a year ago | 14 | mit | Jupyter Notebook | |||||
How to do Bayesian statistical modelling using numpy and PyMC3 | ||||||||||
Sklearn Bayes | 439 | 2 years ago | 20 | mit | Jupyter Notebook | |||||
Python package for Bayesian Machine Learning with scikit-learn API | ||||||||||
Bayesmadesimple | 282 | 3 years ago | 3 | Jupyter Notebook | ||||||
Code for a tutorial on Bayesian Statistics by Allen Downey. | ||||||||||
Scipy2014_tutorial | 281 | 4 years ago | 1 | Jupyter Notebook | ||||||
Tutorial: Bayesian Statistical Analysis in Python | ||||||||||
Bayes_computing_course | 208 | 2 years ago | 1 | mit | Jupyter Notebook | |||||
Trieste | 170 | 5 days ago | 25 | January 31, 2023 | 93 | apache-2.0 | Python | |||
A Bayesian optimization toolbox built on TensorFlow | ||||||||||
Tutorials | 168 | 2 years ago | HTML | |||||||
Tutorials on phylogenetic and phylogenomic inference | ||||||||||
Mcmc_pydata_london_2019 | 75 | 3 years ago | 2 | mit | Jupyter Notebook | |||||
PyData London 2019 Tutorial on Markov chain Monte Carlo with PyMC3 | ||||||||||
Tutorial | 63 | 2 years ago | 3 | gpl-3.0 | Jupyter Notebook | |||||
Tutorial on Bayesian tests for Machine Learning |
Welcome to "Bayesian Modelling in Python" - a tutorial for those interested in learning how to apply bayesian modelling techniques in python (PYMC3). This tutorial doesn't aim to be a bayesian statistics tutorial - but rather a programming cookbook for those who understand the fundamental of bayesian statistics and want to learn how to build bayesian models using python. The tutorial sections and topics can be seen below.
Section 1: Estimating model parameters
Section 2: Model checking & comparison
Section 3: Hierarchal modeling
Section 4: Bayesian regression
Section 5: Bayesian survival analysis
Section 6: Bayesian A/B tests
Statistics is a topic that never resonated with me throughout university. The frequentist techniques that we were taught (p-values etc) felt contrived and ultimately I turned my back on statistics as a topic that I wasn't interested in.
That was until I stumbled upon Bayesian statistics - a branch to statistics quite different from the traditional frequentist statistics that most universities teach. I was inspired by a number of different publications, blogs & videos that I would highly recommend any newbies to bayesian stats to begin with. They include:
I created this tutorial in the hope that others find it useful and it helps them learn Bayesian techniques just like the above resources helped me. I hope you find it useful and I'd welcome any corrections/comments/contributions from the community.
This tutorial is actively being worked on. I'm keen to get feedback and welcome ideas/contributions.