Bayesian Reasoning and Machine Learning by David Barber

Book Contents :-
PART-I INFERENCE IN PROBABILISTIC MODELS 1. Probabilistic Reasoning 2. Basic Graph Concepts 3. Belief Networks 4. Graphical Models 5. Efficient Inference in Trees 6. The Junction Tree Algorithm 7. Making Decisions PART-II LEARNING IN PROBABILISTIC MODELS 8. Statistics for Machine Learning 9. Learning as Inference 10. Naive Bayes 11. Learning with Hidden Variables 12. Bayesian Model Selection PART-III MACHINE LEARNING 13. Machine Learning Concepts 14. Nearest Neighbour Classification 15. Unsupervised Linear Dimension Reduction

About this book :-
"Bayesian Reasoning and Machine Learning" by David Barber is a comprehensive guide to applying "Bayesian methods" in modern "machine learning". The book introduces probabilistic thinking and demonstrates how uncertainty can be formally modeled and used to make predictions. Written in a clear and structured style, it is suitable for students, researchers, and practitioners seeking a deep understanding of probabilistic modeling. The book covers essential concepts such as probability theory, Bayesian inference, "graphical models", approximate inference, and learning algorithms. It also explains advanced techniques like Monte Carlo methods, variational inference, and decision theory. Each topic is illustrated with practical examples, showing how Bayesian reasoning can be applied to real-world problems in areas like computer vision, natural language processing, and robotics. Emphasis is placed on connecting theory with practical implementation, including coding exercises and model evaluation. Overall, this book equips readers with the skills to develop "probabilistic models", perform "inference", and implement "machine learning algorithms" that handle uncertainty effectively. By combining theory, examples, and practical guidance, it provides a strong foundation in Bayesian reasoning for anyone looking to enhance their understanding of modern "data science", statistical modeling, and intelligent systems. It is an essential resource for applied and theoretical learners alike.

Book Detail :-
Title: Bayesian Reasoning and Machine Learning by David Barber
Publisher: Cambridge University Press
Year: 2025
Pages: 768
Type: PDF
Language: English
ISBN-10 #: 0521518148
ISBN-13 #: 978-0521518147
License: External Educational Resource
Amazon: Amazon

About Author :-
The author David Barber is a British mathematician and computer scientist known for his work in "machine learning" and "Bayesian reasoning". He earned a "BA in Mathematics" from the University of Cambridge and a "PhD in Theoretical Physics" from the University of Edinburgh. Barber is a professor at "University College London (UCL)", where he leads research in probabilistic modeling and artificial intelligence. His expertise spans "probabilistic modelling, graphical models, Bayesian methods, data analysis, and algorithm implementation". Barber develops both theoretical foundations and practical tools for learning from data under uncertainty. His work is widely used in research and applications of machine learning, AI, and statistics.

Similar Computational & Bayesian Statistics Books