This is an introduction to the theory and the practices of Bayesian belief networks for graduate students. This is an area of research where statistics, probability and artificial intelligence meet. The book starts by explaining how Bayesian belief networks came about and then describes in detail how they work. Each chapter has examples and exercises at the end to aid learning, and the largest chapter focuses on real-life applications in medicine, telecommunications, space technology and control systems. The book also includes a systematic representation of the advances which are likely to play a central role in the development of Bayesian belief networks in the future.
