Richard Neapolitan: Difference between revisions
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Dr. Neapolitan's book titled "Probabilistic Reasoning in Expert Systems" is one of the first two books in the field. It is an important book because it clearly defined the field of Bayesian Network for the first time. It is also a very helpful book to readers because it is very well written. But the description about this book on this page does not read smoothly. For example, “P.W. Jones, 1992, "Probabilistic Reasoning in Expert Systems” is not a complete sentence. This kind of problems have be |
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|education = [[University of Illinois]] {{small|([[Bachelor of Science|BS]])}}<br>[[Illinois Institute of Technology]] {{small|([[Master of Science|MS]], [[Doctor of Philosophy|PhD]])}} |
|education = [[University of Illinois]] {{small|([[Bachelor of Science|BS]])}}<br>[[Illinois Institute of Technology]] {{small|([[Master of Science|MS]], [[Doctor of Philosophy|PhD]])}} |
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== Early life and education == |
== Early life and education == |
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Neapolitan grew up in the 1950s and 1960s in [[Westchester, Illinois]].{{ |
Neapolitan grew up in the 1950s and 1960s in [[Westchester, Illinois]].<ref>{{cite web|title=Who Lives at 1637 Norfolk, Westchester, Il|url=https://www.spokeo.com/IL/Westchester/1637-Norfolk-Ave|website=spokeo}}</ref> He attended the [[Illinois Institute of Technology]] for his doctoral work, obtaining a PhD in 1974.<ref>{{MathGenealogy|id=105207}}</ref> |
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== Research == |
== Research == |
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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 network]]s.{{ |
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 network]]s.<ref name="dawn3">{{cite journal|last1=Holmes|first1=Dawn|title=Interview with Richard Neapolitan|date=2008|url=https://www.kent.ac.uk/secl/researchcentres/reasoning/TheReasoner/vol2/TheReasoner-2(7)-screen.pdf}}</ref> |
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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., Neapolitan 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 a treatise on the use of the classical approach to probability versus the Bayesian approach in artificial intelligence at the 1988 Workshop.<ref>{{cite journal|last1=Levitt|first1=Todd|title=Workshop Report: Uncertainty in Artificial Intelligence|journal=AI Magazine|date=1988|volume=9|issue=4|url=https://pdfs.semanticscholar.org/ea86/7e76d7c7e7c4aa9173854b8129c649ff3b2c.pdf}}</ref>. 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''<ref name="neoexpert" />. |
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 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 a treatise on the use of the classical approach to probability versus the Bayesian approach in artificial intelligence at the 1988 Workshop.<ref>{{cite journal|last1=Levitt|first1=Todd|title=Workshop Report: Uncertainty in Artificial Intelligence|journal=AI Magazine|date=1988|volume=9|issue=4|url=https://pdfs.semanticscholar.org/ea86/7e76d7c7e7c4aa9173854b8129c649ff3b2c.pdf}}</ref>. 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''<ref name="neoexpert" />. |
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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''.<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}}</ref>. The text defines a causal (Bayesian) network, and proves a theorem showing that a [[directed acyclic graph]] <math>G</math> and a discrete probability distribution <math>P</math> together constitute a Bayesian network if and only if <math>P</math> is equal to the product of its conditional distributions in <math>G</math>. 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.<ref name="neolearn">{{cite book|last1=Neapolitan|first1=Richard|title=Learning Bayesian Networks|date=2003|publisher=Prentice Hall|isbn=978-0130125347}}</ref> |
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''.<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}}</ref>. The text defines a causal (Bayesian) network, and proves a theorem showing that a [[directed acyclic graph]] <math>G</math> and a discrete probability distribution <math>P</math> together constitute a Bayesian network if and only if <math>P</math> is equal to the product of its conditional distributions in <math>G</math>. 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.<ref name="neolearn">{{cite book|last1=Neapolitan|first1=Richard|title=Learning Bayesian Networks|date=2003|publisher=Prentice Hall|isbn=978-0130125347}}</ref> |
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Neapolitan's "Probabilistic Reasoning in Expert Systems"<ref name="neoexpert" /> and [[Judea Pearl]]'s "Probabilistic Reasoning in Intelligent Systems"<ref name="pearl book">{{cite book|last1=Pearl|first1=Judea|title=Probabilistic Reasoning in Intelligent Systems|date=1988|publisher=Morgan Kaufmann|isbn=978-1558604797}}</ref> 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;<ref>{{cite journal|last1=Charniak|first1=Eugene|title=Bayesian Networks Without Tears|journal=AI Magazine|date=1991|pages=57|url=http://leonidzhukov.net/hse/2013/stochmod/papers/Charniak_91.pdf}}</ref> P.W. Jones, who wrote a review of "Probabilistic Reasoning in Expert Systems"in 1992;<ref name="Jones review">{{cite journal|last1=Jones|first1=P.W.|title=Review of Probabilistic Reasoning in Expert Systems, Theory and Algorithms|journal=Technometrics|date=1992|volume=32|issue=1|url=https://www.tandfonline.com/doi/abs/10.1080/00401706.1992.10485240}}</ref> 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;<ref>{{cite journal|last1=Cooper|first1=Gregory|last2=Herskovits|first2=Edward|title=A Bayesian Method for the Induction of Probabilistic Networks from Data|journal=Machine Learning|date=1992|volume=9|pages=312|url=https://link.springer.com/content/pdf/10.1007%2FBF00994110.pdf}}</ref> and Simon Parsons, who, in 1995, compared the two texts and discussed their roles in establishing the field of probabilistic networks.<ref>{{cite web|last1=Parsons|first1=Simon|title=A Review of "Probabilistic reasoning in expert systems — theory and algorithms”|date=1995|url=https://pdfs.semanticscholar.org/2b1d/7f6606f4006eeb2dd3ffd0fc3648d72b9c74.pdf}}</ref> More recently, in 2008, Dawn Holmes discussed Neapolitan's career and the contribution of his first text.<ref name="dawn3" /> |
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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'',<ref name="neolearn" /> which is the first book addressing learning Bayesian networks. Other Bayesian network books that 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}}</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}}</ref> which applies Bayesian networks to problems in biology. Neapolitan has also written ''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}}</ref> and (with Xia Jiang) ''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}}</ref> |
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'',<ref name="neolearn" /> which is the first book addressing learning Bayesian networks. Other Bayesian network books that 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}}</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}}</ref> which applies Bayesian networks to problems in biology. Neapolitan has also written ''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}}</ref> and (with Xia Jiang) ''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}}</ref> |
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Revision as of 00:29, 24 April 2018
 | A major contributor to this article appears to have a close connection with its subject. (April 2018) |
 | This article contains promotional content. (April 2018) |
Richard Neapolitan | |
|---|---|
| Born | Richard Eugene Neapolitan |
| Education | University of Illinois (BS) Illinois Institute of Technology (MS, PhD) |
| Scientific career | |
| Fields | mathematics computer science |
Richard Eugene Neapolitan is an American scientist. He is professor emeritus of computer science at Northeastern Illinois University and professor of bioinformatics at Northwestern University.
Early life and education
Neapolitan grew up in the 1950s and 1960s in Westchester, Illinois.[1] He attended the Illinois Institute of Technology for his doctoral work, obtaining a PhD in 1974.[2]
Research
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.[3]
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 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 a treatise 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[5].
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.[5]. 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 using Bayesian networks and influence diagrams.[6]
Neapolitan's "Probabilistic Reasoning in Expert Systems"[5] 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.[3]
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,[6] 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]
References
- ^ "Who Lives at 1637 Norfolk, Westchester, Il". spokeo.
- ^ Richard Neapolitan at the Mathematics Genealogy Project
- ^ a b Holmes, Dawn (2008). "Interview with Richard Neapolitan" (PDF).
{{cite journal}}: Cite journal requires|journal=(help) - ^ Levitt, Todd (1988). "Workshop Report: Uncertainty in Artificial Intelligence" (PDF). AI Magazine. 9 (4).
- ^ a b c Neapolitan, Richard (1989). Probabilistic Reasoning in Expert Systems: Theory and Algorithms. Wiley. ISBN 978-0471618409.
- ^ a b Neapolitan, Richard (2003). Learning Bayesian Networks. Prentice Hall. ISBN 978-0130125347.
- ^ Pearl, Judea (1988). Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann. ISBN 978-1558604797.
- ^ Charniak, Eugene (1991). "Bayesian Networks Without Tears" (PDF). AI Magazine: 57.
- ^ Jones, P.W. (1992). "Review of Probabilistic Reasoning in Expert Systems, Theory and Algorithms". Technometrics. 32 (1).
- ^ Cooper, Gregory; Herskovits, Edward (1992). "A Bayesian Method for the Induction of Probabilistic Networks from Data" (PDF). Machine Learning. 9: 312.
- ^ Parsons, Simon (1995). "A Review of "Probabilistic reasoning in expert systems — theory and algorithms"" (PDF).
- ^ 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.
