Richard Neapolitan Explained
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.[1]
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.[2] He served the majority of his academic career at NEIU, including becoming Chair of Computer Science in 2002.[3]
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.[4] 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.
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 using Bayesian networks and influence diagrams.
[6] Neapolitan's "Probabilistic Reasoning in Expert Systems" and
Judea Pearl's "Probabilistic Reasoning in Intelligent Systems"
[7] have been widely recognized as formalizing the field of Bayesian networks, as seen in the works of
Eugene Charniak, who, in 1991, noted both texts as the source for Bayesian network inference algorithms;
[8] P.W. Jones, who wrote a review of "Probabilistic Reasoning in Expert Systems"in 1992;
[9] Cooper and Herskovits, who credit Neapolitan's text and Pearl's text for formalizing the theory of belief networks in their 1992 paper that developed the score-based method for learning Bayesian networks from data;
[10] and Simon Parsons, who, in 1995, compared the two texts and discussed their roles in establishing the field of probabilistic networks.
[11] More recently, in 2008, Dawn Holmes discussed Neapolitan's career and the contribution of his first text. In the 1990s researchers strived to develop methods that could learn Bayesian networks from data. Neapolitan assimilated these efforts in the 2003 text
Learning Bayesian Networks, which is the first book addressing learning Bayesian networks. Other Bayesian network books that Neapolitan authored include
Probabilistic Methods for Financial and Marketing Informatics,
[12] which applies Bayesian networks to problems in finance and marketing; and
Probabilistic Methods for Bioinformatics,
[13] which applies Bayesian networks to problems in biology. Neapolitan has also written
Foundations of Algorithms[14] and (with Xia Jiang)
Artificial Intelligence: With an Introduction to Machine Learning.
[15] Notes and References
- Holmes. Dawn. Interview with Richard Neapolitan. July 2008. The Reasoner. 2. 7. 4–9.
- Book: Northeastern Illinois University 1981 Yearbook. Chicago, IL: Northeastern Illinois University. 1981.
- Web site: Northeastern Illinois University 2002-2003 Academic Catalog.
- Levitt. Todd. Workshop Report: Uncertainty in Artificial Intelligence. AI Magazine. 1988. 9. 4. 10.1609/aimag.v9i4.957. 2867172. https://web.archive.org/web/20180402101443/https://pdfs.semanticscholar.org/ea86/7e76d7c7e7c4aa9173854b8129c649ff3b2c.pdf. dead. 2018-04-02.
- Book: Neapolitan. Richard. Probabilistic Reasoning in Expert Systems: Theory and Algorithms. 1989. Wiley. 978-0471618409.
- Book: Neapolitan. Richard. Learning Bayesian Networks. 2003. Prentice Hall. 978-0130125347.
- Book: Pearl. Judea. Probabilistic Reasoning in Intelligent Systems. registration. 1988. Morgan Kaufmann. 978-1558604797.
- Charniak. Eugene. Bayesian Networks Without Tears. AI Magazine. 1991. 57.
- Jones. P.W.. Review of Probabilistic Reasoning in Expert Systems, Theory and Algorithms. Technometrics. 1992. 32. 1. 10.1080/00401706.1992.10485240. 250564157 .
- Cooper. Gregory. Herskovits. Edward. A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning. 1992. 9. 4. 309–347. 10.1007/BF00994110. 6047868 .
- Parsons . Simon . 10.1093/logcom/5.4.547 . 4 . Journal of Logic and Computation . 547–549 . Review of Probabilistic Reasoning in Expert Systems – Theory and Algorithms, by Richard E. Neapolitan . https://web.archive.org/web/20180402101417/https://pdfs.semanticscholar.org/2b1d/7f6606f4006eeb2dd3ffd0fc3648d72b9c74.pdf . 2018-04-02 . 5 . 1995.
- Book: Neapolitan. Richard. Jiang. Xia. Probabilistic Methods for Financial and Marketing Informatics. 2007. Morgan Kaufmann. San Francisco, CA. 978-0-12-370477-1.
- Book: Neapolitan. Richard. Probabilistic Methods for Bioinformatics. 2009. Morgan Kaufmann. San Francisco, CA. 978-0-12-370476-4.
- Book: Neapolitan. Richard. Foundations of Algorithms. 2015. Jones and Bartlett. Burlington, MA. 978-1-284-04919-0.
- Book: Neapolitan. Richard. Jiang. Xia. Artificial Intelligence: With an Introduction to Machine Learning. 2018. CRC Press. Boca Raton, FL. 9781138502383.
