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 ...

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

A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool ...

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 ...

Introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written using Jupyter Notebook so that you can run and modify the code in your browser. What ...

An illustration of a magnifying glass. An illustration of a magnifying glass.