"Probabilistic Reasoning in Intelligent Systems"

"Probabilistic Reasoning in Intelligent Systems" refers to methods that allow computers to make informed decisions under uncertainty. By using probability, these systems assess the likelihood of different outcomes based on available data, helping them handle incomplete or ambiguous information effectively. This approach enables intelligent systems, like diagnostic tools or recommendation engines, to update their beliefs as new information arrives, leading to more accurate and adaptable decisions. Essentially, it provides a mathematical framework for machines to think and reason in a way that accounts for uncertainty, much like humans do when making judgments based on incomplete clues.