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Richard Neapolitan.

Richard E. Neapolitan

Richard E. Neapolitan is an American scientist. He is professor emeritus of computer science at Northeastern Illinois University, and professor of bioinformatics at Northwestern University.

Research

Dr. Neapolitan is most well-known for his role in establishing the field of Bayesian networks. In the 1980s, researchers from cognitive science (e.g., Judea Pearl), computer science (e.g., Peter Cheeseman and Lotfi Zadeh), decision analysis (e.g., Ross Shachter), medicine (e.g., David Heckerman and Gregory Cooper), mathematics and statistics (e.g., Richard Neapolitan and David Spiegelhalter) and philosophy (e.g., Henry Kyburg) met at the newly formed Workshop on Uncertainty in Artificial Intelligence (now a conference) to discuss how to best perform uncertain inference in artificial intelligence. At that workshop researchers developed and discussed graphical models that could represent large joint probability distributions, but they (including Richard) did not always mean exactly the same thing. Some argued that the edges in the graph must be causal; others argued they did not. Dr. Neapolitan rigorously defined a Bayesian network ( called a causal network at the time), and proved a theorem showing that a directed acyclic graph G and a discrete probability distribution P is a Bayesian network if and only if P is equal to the product of its conditional distributions in G. He made this result the basis of the seminal 1989 text Probabilistic Reasoning in Expert Systems^[1]. The text also includes methods for doing inference in Bayesian networks and a philosophical treatise on the difference between the relative frequency approach to probability and the Bayesian approach. While writing this text, he and statistician Sandy Zabell met regularly over coffee at the University of Chicago to discuss the philosophy of probability. At this time, Dr. Neapolitan also communicated by mail with British statistician Dennis Lindley concerning the foundations of probability. These discussions and communications helped Dr. Neapolitan articulate the views on probability which appear in the text, and which provide a foundation for the use of probability in artificial intelligence. The text became the underpinning for the field of Bayesian networks. Bayesian networks have arguably become the standard for handling uncertain inference in AI, and many AI applications have been developed using them^[2].

In the 1990s researchers strived to develop methods that could learn Bayesian networks from data. Dr Neapolitan assimilated these efforts in the 2004 text Learning Bayesian Networks^[2]. Other Bayesian network books Dr. Neapolitan authored include Probabilistic Methods for Financial and Marketing Informatics^[3], which applies Bayesian networks to problems in finance and marketing; and Probabilistic Methods for Bioinformatics^[4], which applies Bayesian networks to problems in biology. Dr. Neapolitan wrote several books outside the field of Bayesian networks. He authored Foundations of Algorithms^[5], which is one of the most widely used algorithms world wide, and which has been translated to Polish, Korean, and Chinese. Recently, his colleague Xia Jiang noted to him the lack of an accessible textbook that covered the current most important areas of artificial intelligence. So, together they authored Artificial Intelligence: With an Introduction to Machine Learning^[6]. The book covers the five approaches to artificial intelligence which have been successfully applied to solving real world problems. They are the logical intelligence, probabilistic intelligence, emergent intelligence, neural intelligence, and language understanding.

Dr. Neapolitan has been invited to give lectures and conduct classes worldwide including the course Probabilistic Causality at the CEU Summer Institute in Budapest, Hungary; August, 2008^[7], the Google seminar Towards Pragmatic Statistics at Google Pittsburgh in 2009^[8], and the tutorial Learning Bayesian Networks at the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)^[9], San Jose 2007]. Dawn Holmes interviewed Dr. Neapolitan for the Reasoner in July, 2008 to ascertain his views on the philosophy of science and statistics^[10].

Richard Neapolitan's interests and contributions to science extend beyond mathematics and computer science. For example, in the area of philosophy of science he published the paper "A Limiting Frequency Approach to Probability Based on the Weak Law of Large Numbers"^[11]. This paper extends the early work of Richard von Mises^[12], which derived the rules of probability theory by assuming the relative frequency of occurrence of an event definitely converges to the probability. Dr. Neapolitan derived those rules by assuming convergence only in the sense of the weak law of large numbers. This assumption is more consistent with our modern day notion of probability as a relative frequency. In the area of psychology, with colleagues Dr. Neapolitan wrote "The Cognitive Processing of Causal Knowledge"^[13]. This paper shows how studies by Piaget indicate that humans learn causal structure by observing the same independencies and dependencies as those used by certain algorithms for learning the structure of a Bayesian network. Dr. Neapolitan has written numerous papers in the areas of medicine and biomedical informatics. One notable contribution is "Pan-cancer analysis of TCGA Data Reveals Notable Signaling Pathways"^[14]. This paper obtains results using TCGA data which indicate 7 signal transduction pathways account for much of the mechanisms of cancer in 10 types of cancer.

Biography

Richard grew up in the 1950's and 1960's in Westchester, Illinois, which is a western suburb of Chicago. His cousin author Phil Caputo grew up a few blocks away. One of his best friends was actor Robert Z'Dar who also grew up a few blocks away. It was a magical time growing up in the U.S. in the 1950s and 1960s. Richard recalls playing sandlot baseball morning, afternoon, and evening. Another friend of his Michael Gubbins wrote the book Scars to the Collection^[15] about their exploits growing up in Westchester. Young Richard dreamed of being a scientist growing up, and eventually obtained a Ph.D. in mathematics. However, in the late 1970s there was a recession and a glut of Ph.D. mathematicans. Rich joined his friends C.J. Polainer and Bobby Z (Robert Z'Dar) in Los Angeles and pursued a brief career as a male model. Eventually, he obtained a faculty position at Northeastern Illinos University and returned to Chicago.

Once situated in a university, Dr. Neapolitan pursued his research career. He became particularly interested in applying concrete mathematical methods to artificial intelligence, and joined the uncertainty in artificial intelligence workshop. As discussed above, his research efforts then led to his writing Probabilistic Reasoning in Expert Systems^[1]. After writing that text, Dr. Neapolitan devoted his research career to artificial intelligence and related fields, resulting in the books and papers mentioned above. He continues in this endeavor today.

References

^ ^a ^b Neapolitan, Richard (1989). Probabilistic Reasoning in Expert Systems: Theory and Algorithms. Wiley. ISBN 978-0471618409.
^ ^a ^b Neapolitan, Richard (2004). Learning Bayesian Networks. Prentice Hall. ISBN 978-0130125347.
^ Neapolitan, Richard; Jiang, Xia (2007). Probabilistic Methods for Financial and Marketing Informatics. San Francisco, CA: Morgan Kaufmann. ISBN 978-0-12-370477-1.
^ Neapolitan, Richard (2009). Probabilistic Methods for Bioinformatics. San Francisco, CA: Morgan Kaufmann. ISBN 978-0-12-370476-4.
^ Neapolitan, Richard (2015). Foundations of Algorithms. Burlington, MA: Jones and Bartlett. ISBN 978-1-284-04919-0.
^ Neapolitan, Richard; Jiang, Xia (2018). Artificial Intelligence: With an Introduction to Machine Learning. Boca Raton, FL: CRC Press. ISBN 9781138502383.
^ "CEU Summer Institute".
^ "Collaborative Management of Talks".
^ "Learning Bayesian Networks". Videolectures.net.
^ "The Reasoner" (PDF).
^ Neapolitan, Richard (1992). "A Limiting Frequency Approach to Probability Based on the Weak Law of Large Numbers". Philosophy of Science. 59 (3): 389–407.
^ von Mises, Richard (1928). Probability, Statistics, and Truth. Dover Publications. ISBN 0486242145.
^ Morris, Scott; Cork, Doug; Neapolitan, Richard (1997). "The Cognitive Processing of Causal Knowledge". Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence.
^ Jiang, Xia; Horvath, Kurt; Neapolitan, Richard (2015). "Pan-cancer analysis of TCGA Data Reveals Notable Signaling Pathways". BMC Cancer. 15 (516).
^ Gubbins, Michael (2017). Scars to the Collection. Amazon ebook.

Richard Neapolitan

[neoexpert-1] Neapolitan, Richard (1989). Probabilistic Reasoning in Expert Systems: Theory and Algorithms. Wiley. ISBN 978-0471618409.

[neolearn-2] Neapolitan, Richard (2004). Learning Bayesian Networks. Prentice Hall. ISBN 978-0130125347.

[financialtwo-3] Neapolitan, Richard; Jiang, Xia (2007). Probabilistic Methods for Financial and Marketing Informatics. San Francisco, CA: Morgan Kaufmann. ISBN 978-0-12-370477-1.

[bioinfo-4] Neapolitan, Richard (2009). Probabilistic Methods for Bioinformatics. San Francisco, CA: Morgan Kaufmann. ISBN 978-0-12-370476-4.

[5] Neapolitan, Richard (2015). Foundations of Algorithms. Burlington, MA: Jones and Bartlett. ISBN 978-1-284-04919-0.

[AI-6] Neapolitan, Richard; Jiang, Xia (2018). Artificial Intelligence: With an Introduction to Machine Learning. Boca Raton, FL: CRC Press. ISBN 9781138502383.

[CEU-7] "CEU Summer Institute".

[8] "Collaborative Management of Talks".

[9] "Learning Bayesian Networks". Videolectures.net.

[10] "The Reasoner" (PDF).

[limfreq-11] Neapolitan, Richard (1992). "A Limiting Frequency Approach to Probability Based on the Weak Law of Large Numbers". Philosophy of Science. 59 (3): 389–407.

[vonmises-12] von Mises, Richard (1928). Probability, Statistics, and Truth. Dover Publications. ISBN 0486242145.

[causalproc-13] Morris, Scott; Cork, Doug; Neapolitan, Richard (1997). "The Cognitive Processing of Causal Knowledge". Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence.

[pancancer-14] Jiang, Xia; Horvath, Kurt; Neapolitan, Richard (2015). "Pan-cancer analysis of TCGA Data Reveals Notable Signaling Pathways". BMC Cancer. 15 (516).

[gubbins-15] Gubbins, Michael (2017). Scars to the Collection. Amazon ebook.

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@@ Line 8: / Line 8: @@
 ----
-[[File:rich3.jpg|thumb|alt=Richard Neapolitan.|Richard Neapolitan]]
+[[File:rich3.jpg|thumb|alt=Richard Neapolitan.|'''<big>Richard E. Neapolitan</big>''']]
-'''Richard E. Neapolitan''' is an American scientist. He is professor emeritus of computer science at [http://homepages.neiu.edu/~reneapol/ Northeastern Illinois University], professor of bioinformatics at [http://www.feinberg.northwestern.edu/faculty-profiles/az/profile.html?xid=25824 Northwestern University], and president of Bayesian Network Solutions.
+'''Richard E. Neapolitan''' is an American scientist. He is professor emeritus of computer science at [http://homepages.neiu.edu/~reneapol/ Northeastern Illinois University], and professor of bioinformatics at [http://www.feinberg.northwestern.edu/faculty-profiles/az/profile.html?xid=25824 Northwestern University].
+== Research ==
-Dr. Neapolitan is most well-known for his role in establishing the field of Bayesian networks. In the 1980s, researchers from cognitive science (e.g., Judea Pearl), computer science (e.g., Peter Cheeseman and Lotfi Zadeh), decision analysis (e.g., Ross Shachter), medicine (e.g., David Heckerman and Gregory Cooper), mathematics and statistics (e.g., Richard Neapolitan and David Spiegelhalter) and philosophy (e.g., Henry Kyburg) met at the newly formed Workshop on Uncertainty in Artificial Intelligence (now a conference) to discuss how to best perform uncertain inference in artificial intelligence. Dr. Neapolitan's seminal text ''Probabilistic Reasoning in Expert Systems''<ref name="neoexpert">{{cite book|last1=Neapolitan|first1=Richard|title=Probabilistic Reasoning in Expert Systems: Theory and Algorithms|date=1989|publisher=Wiley|isbn=978-0471618409|url=https://www.amazon.com/Probabilistic-Reasoning-Expert-Systems-Algorithms/dp/0471618403/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr=}}</ref> integrated many of the results of these discussions into the field we now call [[Bayesian_network|Bayesian Networks]]. Bayesian networks have arguably become the standard for handling uncertain inference in AI, and many AI applications have been developed using them<ref name="neolearn">{{cite book|last1=Neapolitan|first1=Richard|title=Learning Bayesian Networks|date=2004|publisher=Prentice Hall|isbn=978-0130125347|url=https://www.pearson.com/us/higher-education/program/Neapolitan-Learning-Bayesian-Networks/PGM134910.html}}</ref>.
+Dr. Neapolitan is most well-known for his role in establishing the field of [[Bayesian_network|Bayesian networks]]. In the 1980s, researchers from cognitive science (e.g., Judea Pearl), computer science (e.g., Peter Cheeseman and Lotfi Zadeh), decision analysis (e.g., Ross Shachter), medicine (e.g., David Heckerman and Gregory Cooper), mathematics and statistics (e.g., Richard Neapolitan and David Spiegelhalter) and philosophy (e.g., Henry Kyburg) met at the newly formed Workshop on Uncertainty in Artificial Intelligence (now a conference) to discuss how to best perform uncertain inference in artificial intelligence. At that workshop researchers developed and discussed graphical models that could represent large joint probability distributions, but they (including Richard) did not always mean exactly the same thing. Some argued that the edges in the graph must be causal; others argued they  did not. Dr. Neapolitan rigorously defined a Bayesian network ( called a causal network at the time), and proved a theorem showing that a directed acyclic graph ''G'' and a discrete probability distribution ''P'' is a Bayesian network if and only if ''P'' is equal to the product of its conditional distributions in ''G''. He made this result the basis of the seminal 1989 text ''Probabilistic Reasoning in Expert Systems''<ref name="neoexpert">{{cite book|last1=Neapolitan|first1=Richard|title=Probabilistic Reasoning in Expert Systems: Theory and Algorithms|date=1989|publisher=Wiley|isbn=978-0471618409|url=https://www.amazon.com/Probabilistic-Reasoning-Expert-Systems-Algorithms/dp/0471618403/ref=tmm_hrd_swatch_0?_encoding=UTF8&qid=&sr=}}</ref>. The text also includes methods for doing inference in Bayesian networks and a philosophical treatise on the difference between the relative frequency approach to probability and the Bayesian approach. While writing this text, he and statistician [http://www.statistics.northwestern.edu/people/faculty/sandy-zabell.html Sandy Zabell] met regularly over coffee at the University of Chicago to discuss the philosophy of probability. At this time, Dr. Neapolitan also communicated by mail with British statistician [[Dennis_Lindley|Dennis Lindley]] concerning the foundations of probability. These discussions and communications helped Dr. Neapolitan articulate the views on probability which appear in the text, and which provide a foundation for the use of probability in artificial intelligence. The text became the underpinning for the field of Bayesian networks. Bayesian networks have arguably become the standard for handling uncertain inference in AI, and many AI applications have been developed using them<ref name="neolearn">{{cite book|last1=Neapolitan|first1=Richard|title=Learning Bayesian Networks|date=2004|publisher=Prentice Hall|isbn=978-0130125347|url=https://www.pearson.com/us/higher-education/program/Neapolitan-Learning-Bayesian-Networks/PGM134910.html}}</ref>.
 In the 1990s researchers strived to develop methods that could learn Bayesian networks from data. Dr Neapolitan assimilated these efforts in the 2004 text ''Learning Bayesian Networks''<ref name="neolearn" />. Other Bayesian network books Dr. Neapolitan authored include ''Probabilistic Methods for Financial and Marketing Informatics''<ref name="financialtwo">{{cite book|last1=Neapolitan|first1=Richard|last2=Jiang|first2=Xia|title=Probabilistic Methods for Financial and Marketing Informatics|date=2007|publisher=Morgan Kaufmann|location=San Francisco, CA|isbn=978-0-12-370477-1|url=https://www.elsevier.com/books/probabilistic-methods-for-financial-and-marketing-informatics/neapolitan/978-0-12-370477-1}}</ref>, which applies Bayesian networks to problems in finance and marketing; and ''Probabilistic Methods for Bioinformatics''<ref name="bioinfo">{{cite book|last1=Neapolitan|first1=Richard|title=Probabilistic Methods for Bioinformatics|date=2009|publisher=Morgan Kaufmann|location=San Francisco, CA|isbn=978-0-12-370476-4|url=https://www.elsevier.com/books/probabilistic-methods-for-bioinformatics/neapolitan/978-0-12-370476-4}}</ref>, which applies Bayesian networks to problems in biology. Dr. Neapolitan wrote several books outside the field of Bayesian networks. He authored ''Foundations of Algorithms''<ref>{{cite book|last1=Neapolitan|first1=Richard|title=Foundations of Algorithms|date=2015|publisher=Jones and Bartlett|location=Burlington, MA|isbn=978-1-284-04919-0|url=http://www.jblearning.com/catalog/9781284049190/}}</ref>, which is one of the most widely used algorithms world wide, and which has been translated to Polish, Korean, and Chinese. Recently, his colleague Xia Jiang noted to him the lack of an accessible textbook that covered the current most important areas of artificial intelligence. So, together they authored ''Artificial Intelligence: With an Introduction to Machine Learning''<ref name="AI">{{cite book|last1=Neapolitan|first1=Richard|last2=Jiang|first2=Xia|title=Artificial Intelligence: With an Introduction to Machine Learning|date=2018|publisher=CRC Press|location=Boca Raton, FL|isbn=9781138502383|url=https://www.crcpress.com/Contemporary-Artificial-Intelligence-Second-Edition/Neapolitan-Jiang/p/book/9781138502383}}</ref>. The book covers the five approaches to artificial intelligence which have been successfully applied to solving real world problems. They are the logical intelligence, probabilistic intelligence, emergent intelligence, neural intelligence, and language understanding.
@@ Line 20: / Line 22: @@
 Richard Neapolitan's interests and contributions to science extend beyond mathematics and computer science. For example, in the area of philosophy of science he published the paper "A Limiting Frequency Approach to Probability Based on the Weak Law of Large Numbers"<ref name="limfreq">{{cite journal|last1=Neapolitan|first1=Richard|title=A Limiting Frequency Approach to Probability Based on the Weak Law of Large Numbers|journal=Philosophy of Science|date=1992|volume=59|issue=3|pages=389-407|url=https://www.jstor.org/stable/188155?seq=1#page_scan_tab_contents}}</ref>. This paper extends the early work of Richard von Mises<ref name="vonmises">{{cite book|last1=von Mises|first1=Richard|title=Probability, Statistics, and Truth|date=1928|publisher=Dover Publications|isbn=0486242145|url=https://www.maa.org/press/maa-reviews/probability-statistics-and-truth}}</ref>, which derived the rules of probability theory by assuming the relative frequency of occurrence of an event definitely converges to the probability. Dr. Neapolitan derived those rules by assuming convergence only in the sense of the weak law of large numbers. This assumption is more consistent with our modern day notion of probability as a relative frequency. In the area of psychology, with colleagues Dr. Neapolitan wrote "The Cognitive Processing of Causal Knowledge"<ref name="causalproc">{{cite journal|last1=Morris|first1=Scott|last2=Cork|first2=Doug|last3=Neapolitan|first3=Richard|title=The Cognitive Processing of Causal Knowledge|journal=Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence|date=1997|url=https://arxiv.org/abs/1302.1563}}</ref>. This paper shows how studies by Piaget indicate that humans learn causal structure by observing the same independencies and dependencies as those used by certain algorithms for learning the structure of a Bayesian network. Dr. Neapolitan has written numerous papers in the areas of medicine and biomedical informatics. One notable contribution is "Pan-cancer analysis of TCGA Data Reveals Notable Signaling Pathways"<ref name="pancancer">{{cite journal|last1=Jiang|first1=Xia|last2=Horvath|first2=Kurt|last3=Neapolitan|first3=Richard|title=Pan-cancer analysis of TCGA Data Reveals Notable Signaling Pathways|journal=BMC Cancer|date=2015|volume=15|issue=516|url=https://bmccancer.biomedcentral.com/articles/10.1186/s12885-015-1484-6}}</ref>. This paper obtains results using TCGA data which indicate 7 signal transduction pathways account for much of the mechanisms of cancer in 10 types of cancer.
+== Biography ==
-Richard grew up in the 1950's and 1960's in Westchester, Illinois, which is a western suburb of Chicago. His cousin author [[Philip_Caputo|Phil Caputo]] grew up a few blocks away. One of his best friends was actor [[Robert Z'Dar]] who also grew up a few blocks away. It was a magical time growing up in the U.S. in the 1950s and 1960s. Richard recalls playing sandlot baseball morning, afternoon, and evening. Another friend of his Michael Gubbins wrote the book ''Scars to the Collection''<ref name="gubbins">{{cite book|last1=Gubbins|first1=Michael|title=Scars to the Collection|date=2017|publisher=Amazon ebook|url=https://www.amazon.com/Scars-Collection-Michael-Anthony-Gubbins-ebook/dp/B0777GT1C3}}</ref> about their exploits growing up in Westchester. Young Richard dreamed of being a scientist growing up, and eventually obtained a Ph.D. in mathematics. However, in the late 1970s there was a recession and a glut of Ph.D. mathematicans. Rich joined his friend Bobby Z (Robert Z'Dar) in Los Angeles and pursued a brief career as a male model. Eventually, he obtained a faculty position at Northeastern Illinos University and returned to Chicago.
+Richard grew up in the 1950's and 1960's in Westchester, Illinois, which is a western suburb of Chicago. His cousin author [[Philip_Caputo|Phil Caputo]] grew up a few blocks away. One of his best friends was actor [[Robert Z'Dar]] who also grew up a few blocks away. It was a magical time growing up in the U.S. in the 1950s and 1960s. Richard recalls playing sandlot baseball morning, afternoon, and evening. Another friend of his Michael Gubbins wrote the book ''Scars to the Collection''<ref name="gubbins">{{cite book|last1=Gubbins|first1=Michael|title=Scars to the Collection|date=2017|publisher=Amazon ebook|url=https://www.amazon.com/Scars-Collection-Michael-Anthony-Gubbins-ebook/dp/B0777GT1C3}}</ref> about their exploits growing up in Westchester. Young Richard dreamed of being a scientist growing up, and eventually obtained a Ph.D. in mathematics. However, in the late 1970s there was a recession and a glut of Ph.D. mathematicans. Rich joined his friends C.J. Polainer and Bobby Z (Robert Z'Dar) in Los Angeles and pursued a brief career as a male model. Eventually, he obtained a faculty position at Northeastern Illinos University and returned to Chicago.
-Once situated in a university, Dr. Neapolitan pursued his research career. He became particularly interested in applying concrete mathematical methods to artificial intelligence, and joined the uncertainty in artificial intelligence workshop. At that workshop researchers from various fields were discussing graphical models that could represent large joint probability distributions, but they (including Richard) did not always mean exactly the same thing. Some argued that the edges in the graph must be causal; others argued they  did not. Dr. Neapolitan finally rigorously defined a Bayesian network ( called a causal network at the time), and proved a theorem showing that a directed acyclic graph ''G'' and a discrete probability distribution ''P'' is a Bayesian network if and only if ''P'' is equal to the product of its conditional distributions in ''G''. He made this result the basis of the 1989 text ''Probabilistic Reasoning in Expert Systems''<ref name="neoexpert" />. The text also includes methods for doing inference in Bayesian networks and a philosophical treatise on the difference between the relative frequency approach to probability and the Bayesian approach. While writing this text, he and statistician [http://www.statistics.northwestern.edu/people/faculty/sandy-zabell.html Sandy Zabell] met regularly over coffee at the University of Chicago to discuss the philosophy of probability. At this time, Dr. Neapolitan also communicated by mail with British statistician [[Dennis_Lindley|Dennis Lindley]] concerning the foundations of probability. These discussions and communications helped Dr. Neapolitan to articulate his views on probability which appear in the text, and which provide a foundation for the use of probability in artificial intelligence. The text became the underpinning for the field of Bayesian networks.
-After writing that text, Dr. Neapolitan devoted his research career to artificial intelligence and related fields, resulting in the books and papers mentioned above. He continues in this endeavor today.
+Once situated in a university, Dr. Neapolitan pursued his research career. He became particularly interested in applying concrete mathematical methods to artificial intelligence, and joined the uncertainty in artificial intelligence workshop. As discussed above, his research efforts then led to his writing ''Probabilistic Reasoning in Expert Systems''<ref name="neoexpert" />. After writing that text, Dr. Neapolitan devoted his research career to artificial intelligence and related fields, resulting in the books and papers mentioned above. He continues in this endeavor today.
 == References ==