This repository includes theoretical notes, slides, and hands-on R examples for exploring Bayesian Linear Regression. It introduces both classical and Bayesian regression methods, showing how to ...

“On this classic ID The Future out of the archive, Dr. Jonathan McLatchie gives us a beginner’s guide to Bayesian thinking and teaches us how it can be used to build a strong cumulative case for ...

Abstract: Conventional neural network-based machine learning algorithms often encounter difficulties in data-limited scenarios or where interpretability is critical. Conversely, Bayesian ...

As we wait for the seabed search for MH370’s wreckage to restart, it’s worth taking the time to reflect about what we’ve learned from the search thus far, and what future scanning will tell us about ...

Currently, ESPresense Companion outputs a single, crisp room assignment per device based on multilateration from RSSI signals. This can be brittle in edge cases (e.g., devices near room boundaries or ...

Abstract: Bayesian Network is a significant graphical model that is used to do probabilistic inference and reasoning under uncertainty circumstances. In many applications, existence of discrete and ...

Department of Engineering, University of Cambridge, Cambridge CB2 1CB2 1PZ, U.K.