Bayesian Reasoning and Machine Learning

Bayesian Reasoning and Machine Learning by David Barber is a comprehensive textbook that introduces the principles of probabilistic modeling and inference using Bayesian methods. The book covers core topics such as graphical models, variational inference, Monte Carlo methods, and machine learning algorithms like HMMs and neural networks from a Bayesian perspective. It is designed for advanced undergraduates, graduate students, and professionals looking to build a deep understanding of the probabilistic foundations behind modern machine learning.