GitHub

Structural causal models with Bayesian estimation and interventional simulation via a concise DSL.

pathmc is a Python package for Bayesian path analysis and structural causal modeling. You write your causal assumptions as a small set of structural equations in a lavaan-inspired formula language, ...
leewayhertz

Bayesian networks in AI: Role in machine learning, example, types and applications

Imagine a scenario where a team of doctors faces a perplexing medical puzzle. A patient shows a range of symptoms, each pointing to multiple possible diseases. How can they navigate this diagnostic ...
IEEE

Bayesian Neural Networks via MCMC: A Python-Based Tutorial

Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...
C&EN

ProcessOptimizer, an Open-Source Python Package for Easy Optimization of Real-World Processes Using Bayesian Optimization: Showcase of Features and Example of Use

ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...
Visual Studio Magazine

Multinomial Naive Bayes Classification Using the scikit Library

A full-code demo from Dr. James McCaffrey of Microsoft Research shows how to predict the type of a college course by analyzing grade counts for each type of course. General naive Bayes classification ...
ascopubs.org

Identifying Confounders Using Bayesian Networks and Estimating Treatment Effect in Prostate Cancer With Observational Data

Randomized controlled trials are considered the golden standard for estimating treatment effect but are costly to perform and not always possible. Observational data, although readily available, is ...