Google colab notebooks used in a lecture on machine learning
-
Updated
Jul 15, 2024 - Jupyter Notebook
Google colab notebooks used in a lecture on machine learning
A Python package for causal inference in quasi-experimental settings
This a simple Customer Lifetime Value analysis using Buy Till You Die Modelling With PyMC Marketing library
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
Distributed differentiable graph computation using PyTensor
Website: Data Umbrella & PyMC open source sessions
Lévy's alpha-stable distribution for the Jax numerical framework
Toolbox for non-linear calibration modeling.
The model predicts the treatment success rate for new TB cases with high accuracy and robustness. Two different approaches: PCA and Bayesian Inference. The Bayesian regression analysis reveals that c_new_sp_tsr and new_sp_fail are significant predictors of the treatment success rate, while other predictors show less certainty in their effects.
Solve ODEs fast, with support for PyMC
demonstration of uni-variate time series prediction by predicting monthly births in Sweden for the next 12 months
Choosing the best golf club using MCMC
Tools for the symbolic manipulation of PyMC models, Theano, and TensorFlow graphs.
Horseshoe regression model fitted in PyMC.
Testing deployment of PyMC models using MLFlow and BentoML.
Project aims at modeling the size distribution of sunspots greater than 60 millionths of a solar hemisphere (MSH) using a truncated log-normal distribution.
Python version of McElreath's Statistical Rethinking package
Demonstrating the use of behavior-driven development (BDD) to Bayesian growth models for assumption tracking.
Add a description, image, and links to the pymc topic page so that developers can more easily learn about it.
To associate your repository with the pymc topic, visit your repo's landing page and select "manage topics."