Probabilistic reasoning in intelligent systems
Book • 1988
This book provides a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty.
It offers a unifying perspective on various AI approaches to uncertainty, including Bayesian networks, and covers applications such as diagnosis, forecasting, and decision support systems.
The book is of special interest to scholars and researchers in AI, decision theory, statistics, and related fields.
It offers a unifying perspective on various AI approaches to uncertainty, including Bayesian networks, and covers applications such as diagnosis, forecasting, and decision support systems.
The book is of special interest to scholars and researchers in AI, decision theory, statistics, and related fields.
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Recommended by Dileep George as a deep technical book connecting probabilistic graphical models to how the brain thinks.
#115 – Dileep George: Brain-Inspired AI
