In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

This self-paced Introduction to Bayesian Network course provides a comprehensive introduction to the theory and practical applications of this powerful tool. Whether you're a complete beginner or have ...

For making probabilistic inferences, a graph is worth a thousand words. A Bayesian network is a graph in which nodes represent entities such as molecules or genes. Nodes that interact are connected by ...

ABSTRACT: This paper investigates the application of machine learning techniques to optimize complex spray-drying operations in manufacturing environments. Using a mixed-methods approach that combines ...

I've read through the documentation and notebooks for Continuous Factors several times, but there is still a big gap between what is written there and what I need to know to create an actual Bayesian ...

Abstract: This paper evaluates the use of different types of models for machine learning and deep learning applied to sentiment analysis on the IMDb movie reviews dataset. Classical approaches, ...