Statistical inference and data mining

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Statistical inference and data mining

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Communications of the ACM archive
Volume 39 , Issue 11 (November 1996) table of contents

Pages: 35 - 41

Year of Publication: 1996

ISSN:0001-0782

Authors

Clark Glymour	University of California, San Diego
David Madigan	University of Washington, Seattle
Daryl Pregibon	AT&T Laboratories
Padhraic Smyth	University of California, Irvine

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 15, Downloads (12 Months): 117, Citation Count: 7

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references cited by index terms collaborative colleagues

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Effort, B. The Jackknife, the Bootstrap, and Other Resampling Plans. Society for Industrial and Applied Mathematics (SIAM), Number 38, Philadelphia, 1982.
	2	David Heckerman, Bayesian Networks for Data Mining, Data Mining and Knowledge Discovery, v.1 n.1, p.79-119, 1997 [doi>10.1023/A:1009730122752]
	3	Hoaglin, D., Mosteller, F., and Tukey, J. Understanding Robust and Exploratory Data Analysis. Wiley, New York, 1983.
	4	Miller, R. Simultaneous Statistical Inference. Springer-Verlag, New York, 1981.
	5	Raftery, A.E. Bayesian model selection in social research. Working Paper 94-12, Center for Studies in Demography and Ecology, Univ. of Washington, Seattle, 1994.
	6	Scheines, R., Spirtes, P., Glymour, C., and Meek, C. TETRAD ll: Tools for Causal Modeling. Users Manual. Erlbaum, Hillsdale, N.J., 1994.
	7	Schervish, M. Theory of Statistics. Springer-Verlag, NewYork, 1995.
	8	Schwartz, G. Estimating the dimension of a model. Ann. Star. 6 (1978), 461-464.
	9	Smith, A.F.M., and Roberts, G.O. Bayesian computation via the Gibb's sampler and related Markov chain Monte Carlo methods. J. R. Stat. Soc., Series B, 55 (1993), 3-23.
	10	Spirtes, P., Glymour, C., and Scheines, R. Causation, Prediction, and Search. Springer-Verlag, New York, 1993.
	11	Spirtes, P., Meek, C., and Richardson, T. Causal inference in the presence of latent variables and selection bias. In Proceedings of the 11 th Conference on Uncertainty in Artificial Intelligence. P. Besnard and S. Hanks, Eds. Morgan Kaufmann Publishers, San Mateo, Calif., 1995, pp. 499-506.
	12	Stigler, S. The History of Statistics. Harvard University Press, Cambridge, Mass., 1986.

CITED BY 7

	Vicki L. Sauter, Intuitive decision-making, Communications of the ACM, v.42 n.6, p.109-115, June 1999
	Albert Y. Zomaya , Tarek El-Ghazawi , Ophir Frieder, Guest Editors' Introduction: Parallel and Distributed Computing for Data Mining, IEEE Concurrency, v.7 n.4, p.11-13, October 1999
	David J. Hand, Statistics and data mining: intersecting disciplines, ACM SIGKDD Explorations Newsletter, v.1 n.1, p.16-19, June 1999
	Khalil M. Ahmed , Nagwa M. El-Makky , Yousry Taha, Effective data mining: a data warehouse-backboned architecture, Proceedings of the 1998 conference of the Centre for Advanced Studies on Collaborative research, p.1, November 30-December 03, 1998, Toronto, Ontario, Canada
	Jyhi-Kong Wey , Pen-Kuan Huang , Hui-Chi Chang , Jiu-Yang Liu, Implementation and management of a mobile phone filtering system, International Journal of Network Management, v.10 n.3, p.165-171, May–June 2000
	Michael Böhlen , Linas Bukauskas , Poul Svante Eriksen , Steffen Lilholt Lauritzen , Arturas Mažeika , Peter Musaeus , Peer Mylov, 3D visual data mining: goals and experiences, Computational Statistics & Data Analysis, v.43 n.4, p.445-469, 28 August 2003
	Petr Hájek , Martin Holena, Formal logics of discovery and hypothesis formation by machine, Theoretical Computer Science, v.292 n.2, p.345-357, 27 January 2003