Bayesian network (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Bayesian network" in English language version.

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8doi.org

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acm.org

dl.acm.org

Pearl J (1994). "A Probabilistic Calculus of Actions". In Lopez de Mantaras R, Poole D (eds.). UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence. San Mateo CA: Morgan Kaufmann. pp. 454–462. arXiv:1302.6835. Bibcode:2013arXiv1302.6835P. ISBN 1-55860-332-8.

arxiv.org

Pearl J (1994). "A Probabilistic Calculus of Actions". In Lopez de Mantaras R, Poole D (eds.). UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence. San Mateo CA: Morgan Kaufmann. pp. 454–462. arXiv:1302.6835. Bibcode:2013arXiv1302.6835P. ISBN 1-55860-332-8.
Shpitser I, Pearl J (2006). "Identification of Conditional Interventional Distributions". In Dechter R, Richardson TS (eds.). Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence. Corvallis, OR: AUAI Press. pp. 437–444. arXiv:1206.6876.
Rebane G, Pearl J (1987). "The Recovery of Causal Poly-trees from Statistical Data". Proceedings, 3rd Workshop on Uncertainty in AI. Seattle, WA. pp. 222–228. arXiv:1304.2736.{{cite book}}: CS1 maint: location missing publisher (link)
Cussens J (2011). "Bayesian network learning with cutting planes" (PDF). Proceedings of the 27th Conference Annual Conference on Uncertainty in Artificial Intelligence: 153–160. arXiv:1202.3713. Bibcode:2012arXiv1202.3713C.
Hoffman, Matthew D.; Gelman, Andrew (2011). "The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo". arXiv:1111.4246 [stat.CO].

berkeley.edu

aima.cs.berkeley.edu

Russell & Norvig 2003, p. 496. Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2.
Russell & Norvig 2003, p. 499. Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13-790395-2.

icsi.berkeley.edu

Dagum P, Luby M (1997). "An optimal approximation algorithm for Bayesian inference". Artificial Intelligence. 93 (1–2): 1–27. CiteSeerX 10.1.1.36.7946. doi:10.1016/s0004-3702(97)00013-1. Archived from the original on 2017-07-06. Retrieved 2015-12-19.

books.google.com

Pearl, Judea (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press. ISBN 978-0-521-77362-1. OCLC 42291253.
Spirtes P, Glymour CN, Scheines R (1993). Causation, Prediction, and Search (1st ed.). Springer-Verlag. ISBN 978-0-387-97979-3.
Verma T, Pearl J (1991). "Equivalence and synthesis of causal models". In Bonissone P, Henrion M, Kanal LN, Lemmer JF (eds.). UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence. Elsevier. pp. 255–270. ISBN 0-444-89264-8.
Neapolitan RE (2004). Learning Bayesian networks. Prentice Hall. ISBN 978-0-13-012534-7.
Pearl J (1988-09-15). Probabilistic Reasoning in Intelligent Systems. San Francisco CA: Morgan Kaufmann. p. 1988. ISBN 978-1-55860-479-7.
Neapolitan RE (1989). Probabilistic reasoning in expert systems: theory and algorithms. Wiley. ISBN 978-0-471-61840-9.

cmu.edu

repository.cmu.edu

Spirtes P, Glymour C (1991). "An algorithm for fast recovery of sparse causal graphs" (PDF). Social Science Computer Review. 9 (1): 62–72. CiteSeerX 10.1.1.650.2922. doi:10.1177/089443939100900106. S2CID 38398322.

doi.org

Ruggeri, Fabrizio; Kenett, Ron S.; Faltin, Frederick W., eds. (2007-12-14). Encyclopedia of Statistics in Quality and Reliability (1 ed.). Wiley. p. 1. doi:10.1002/9780470061572.eqr089. ISBN 978-0-470-01861-3.
Spirtes P, Glymour C (1991). "An algorithm for fast recovery of sparse causal graphs" (PDF). Social Science Computer Review. 9 (1): 62–72. CiteSeerX 10.1.1.650.2922. doi:10.1177/089443939100900106. S2CID 38398322.
Friedman N, Geiger D, Goldszmidt M (November 1997). "Bayesian Network Classifiers". Machine Learning. 29 (2–3): 131–163. doi:10.1023/A:1007465528199.
Friedman N, Linial M, Nachman I, Pe'er D (August 2000). "Using Bayesian networks to analyze expression data". Journal of Computational Biology. 7 (3–4): 601–20. CiteSeerX 10.1.1.191.139. doi:10.1089/106652700750050961. PMID 11108481.
Cooper GF (1990). "The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks" (PDF). Artificial Intelligence. 42 (2–3): 393–405. doi:10.1016/0004-3702(90)90060-d. S2CID 43363498.
Dagum P, Luby M (1993). "Approximating probabilistic inference in Bayesian belief networks is NP-hard". Artificial Intelligence. 60 (1): 141–153. CiteSeerX 10.1.1.333.1586. doi:10.1016/0004-3702(93)90036-b.
Dagum P, Luby M (1997). "An optimal approximation algorithm for Bayesian inference". Artificial Intelligence. 93 (1–2): 1–27. CiteSeerX 10.1.1.36.7946. doi:10.1016/s0004-3702(97)00013-1. Archived from the original on 2017-07-06. Retrieved 2015-12-19.
Bayes T, Price (1763). "An Essay towards solving a Problem in the Doctrine of Chances". Philosophical Transactions of the Royal Society. 53: 370–418. doi:10.1098/rstl.1763.0053.

dslpitt.org

Cussens J (2011). "Bayesian network learning with cutting planes" (PDF). Proceedings of the 27th Conference Annual Conference on Uncertainty in Artificial Intelligence: 153–160. arXiv:1202.3713. Bibcode:2012arXiv1202.3713C.

duke.edu

stat.duke.edu

Cooper GF (1990). "The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks" (PDF). Artificial Intelligence. 42 (2–3): 393–405. doi:10.1016/0004-3702(90)90060-d. S2CID 43363498.

harvard.edu

ui.adsabs.harvard.edu

Pearl J (1994). "A Probabilistic Calculus of Actions". In Lopez de Mantaras R, Poole D (eds.). UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence. San Mateo CA: Morgan Kaufmann. pp. 454–462. arXiv:1302.6835. Bibcode:2013arXiv1302.6835P. ISBN 1-55860-332-8.
Cussens J (2011). "Bayesian network learning with cutting planes" (PDF). Proceedings of the 27th Conference Annual Conference on Uncertainty in Artificial Intelligence: 153–160. arXiv:1202.3713. Bibcode:2012arXiv1202.3713C.

illinois.edu

cogcomp.cs.illinois.edu

D. Roth, On the hardness of approximate reasoning, IJCAI (1993)
D. Roth, On the hardness of approximate reasoning, Artificial Intelligence (1996)

nih.gov

pubmed.ncbi.nlm.nih.gov

Friedman N, Linial M, Nachman I, Pe'er D (August 2000). "Using Bayesian networks to analyze expression data". Journal of Computational Biology. 7 (3–4): 601–20. CiteSeerX 10.1.1.191.139. doi:10.1089/106652700750050961. PMID 11108481.

nips.cc

papers.nips.cc

Scanagatta M, de Campos CP, Corani G, Zaffalon M (2015). "Learning Bayesian Networks with Thousands of Variables". NIPS-15: Advances in Neural Information Processing Systems. Vol. 28. Curran Associates. pp. 1855–1863.
M. Scanagatta, G. Corani, C. P. de Campos, and M. Zaffalon. Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables. In NIPS-16: Advances in Neural Information Processing Systems 29, 2016.

psu.edu

citeseerx.ist.psu.edu

Spirtes P, Glymour C (1991). "An algorithm for fast recovery of sparse causal graphs" (PDF). Social Science Computer Review. 9 (1): 62–72. CiteSeerX 10.1.1.650.2922. doi:10.1177/089443939100900106. S2CID 38398322.
Friedman N, Linial M, Nachman I, Pe'er D (August 2000). "Using Bayesian networks to analyze expression data". Journal of Computational Biology. 7 (3–4): 601–20. CiteSeerX 10.1.1.191.139. doi:10.1089/106652700750050961. PMID 11108481.
Dagum P, Luby M (1993). "Approximating probabilistic inference in Bayesian belief networks is NP-hard". Artificial Intelligence. 60 (1): 141–153. CiteSeerX 10.1.1.333.1586. doi:10.1016/0004-3702(93)90036-b.
Dagum P, Luby M (1997). "An optimal approximation algorithm for Bayesian inference". Artificial Intelligence. 93 (1–2): 1–27. CiteSeerX 10.1.1.36.7946. doi:10.1016/s0004-3702(97)00013-1. Archived from the original on 2017-07-06. Retrieved 2015-12-19.

semanticscholar.org

api.semanticscholar.org

Spirtes P, Glymour C (1991). "An algorithm for fast recovery of sparse causal graphs" (PDF). Social Science Computer Review. 9 (1): 62–72. CiteSeerX 10.1.1.650.2922. doi:10.1177/089443939100900106. S2CID 38398322.
Cooper GF (1990). "The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks" (PDF). Artificial Intelligence. 42 (2–3): 393–405. doi:10.1016/0004-3702(90)90060-d. S2CID 43363498.

tiny-clues.eu

Petitjean F, Webb GI, Nicholson AE (2013). Scaling log-linear analysis to high-dimensional data (PDF). International Conference on Data Mining. Dallas, TX, USA: IEEE.

ucla.edu

bayes.cs.ucla.edu

"The Back-Door Criterion" (PDF). Retrieved 2014-09-18.
"d-Separation without Tears" (PDF). Retrieved 2014-09-18.

ftp.cs.ucla.edu

Pearl J (1985). Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning (UCLA Technical Report CSD-850017). Proceedings of the 7th Conference of the Cognitive Science Society, University of California, Irvine, CA. pp. 329–334. Retrieved 2009-05-01.

web.archive.org

Dagum P, Luby M (1997). "An optimal approximation algorithm for Bayesian inference". Artificial Intelligence. 93 (1–2): 1–27. CiteSeerX 10.1.1.36.7946. doi:10.1016/s0004-3702(97)00013-1. Archived from the original on 2017-07-06. Retrieved 2015-12-19.

wiley.com

onlinelibrary.wiley.com

Ruggeri, Fabrizio; Kenett, Ron S.; Faltin, Frederick W., eds. (2007-12-14). Encyclopedia of Statistics in Quality and Reliability (1 ed.). Wiley. p. 1. doi:10.1002/9780470061572.eqr089. ISBN 978-0-470-01861-3.

worldcat.org

Pearl, Judea (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press. ISBN 978-0-521-77362-1. OCLC 42291253.