IntroductionPurpose of this bookThis book depicts the path from Bayesian inference to deep learning as a single long-form technical volume. There is one central theme: how can we handle uncertainty in ...

Bayesian inference offers a principled mechanism for updating beliefs by combining prior distributions with observed data via Bayes’s theorem. Central to this approach is the specification of a ...

This course introduces the theoretical, philosophical, and mathematical foundations of Bayesian Statistical inference. Students will learn to apply this foundational knowledge to real-world data ...

Active inference, and in particular its recent application to partially observable Markov decision processes, offers a unified mathematical framework for modelling perception, learning, and decision ...

According to GoogleDeepMind, Zoubin Ghahramani explains how uncertainty and probability make real‑world AI decisions safer and more reliable.