Richard Eugene Neapolitan was an American scientist. Neapolitan is most well-known for his role in establishing the use of probability theory in artificial intelligence and in the development of the field Bayesian networks. Biography Neapolitan grew up in the 1950s and 1960s in Westchester, Illinois, which is a western suburb of Chicago. He received a Ph.D. in mathematics from the Illinois Institute of Technology. Neapolitan notes that he was unable to obtain an academic position after obtaining his Ph.D., owing to a glut of mathematicians and a recession in the 1970s, and so he worked as a model and in various computer science related positions. The latter experience enabled him to obtain a faculty position in the Computer Science Department of Northeastern Illinois University (NEIU) in 1980. He served the majority of his academic career at NEIU, including becoming Chair of Computer Science in 2002. Research In the 1980s, researchers from cognitive science (e.g., Judea Pearl), computer science (e.g., Peter C. Cheeseman and Lotfi Zadeh), decision analysis (e.g., Ross Shachter), medicine (e.g., David Heckerman and Gregory Cooper), mathematics and statistics (e.g., Neapolitan, Tod Levitt, and David Spiegelhalter) and philosophy (e.g., Henry Kyburg) met at the newly formed Workshop on Uncertainty in Artificial Intelligence to discuss how to best perform uncertain inference in artificial intelligence. Neapolitan presented an exposition on the use of the classical approach to probability versus the Bayesian approach in artificial intelligence at the 1988 Workshop. A more extensive philosophical treatise on the difference between the two approaches and the application of probability to artificial intelligence appeared in his 1989 text Probabilistic Reasoning in Expert Systems: Theory and Algorithms. Closely related to the issue of representing uncertainty in artificial intelligence, researchers at the Workshop on Uncertainty in Artificial Intelligence developed and discussed graphical models that could represent large joint probability distributions. Neapolitan formulated these efforts into a coherent field in the text Probabilistic Reasoning in Expert Systems: Theory and Algorithms. The text defines a causal (Bayesian) network, and proves a theorem showing that a directed acyclic graph and a discrete probability distribution together constitute a Bayesian network if and only if is equal to the product of its conditional distributions in . The text also includes methods for doing inference in Bayesian networks, and a discussion of influence diagrams, which are Bayesian networks augmented with decision nodes and a value node. Many AI applications have since been developed