Toolbox for non-linear calibration modeling.

Distributed differentiable graph computation using PyTensor

A Python package for causal inference in quasi-experimental settings

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

Lévy's alpha-stable distribution for the Jax numerical framework

Google colab notebooks used in a lecture on machine learning

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

Bayesian models for ODE models in the Systems Biology Markup Language (SBML)

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.

Sprints to contribute to the most popular data and AI Python OSS libraries

Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference

Going through the tutorials for integrating PyMC with ODEs

Website: Data Umbrella & PyMC open source sessions