Research Interests

Bayesian Networks
Maximum Entropy Formalism
Machine Learning
Foundations of Bayesianism
Data Mining, Issues in Statistical Education
Intuitionistic Mathematics

Publications

	Year	Title & Authors	Others	Note
	1998	Reasoning with Incomplete Information in a Multivalued Multiway Causal Tree Using the Maximum Entropy Formalism Dawn E. Holmes and Paul C. Rhodes	International Journal of Intelligent Systems Vol. 13 No. 9 September 1998 pp 841-859
	1998	Using Maximum Entropy to Estimate Missing Information in Tree-like Causal Networks Gerald R. Garside, Dawn E. Holmes, Paul C. Rhodes	Proceedings: 7th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. La Sorbonne, Paris, France, pp359-366
	1998	Efficient Computation of Marginal Probabilities in Multivalued Causal Inverted Multiway Trees given Incomplete Information Dawn E. Holmes, Paul C. Rhodes, Gerald R. Garside	International Journal of Intelligent Systems Vol. 14, No 6, pp 535 – 558
	1999	Efficient Estimation of Missing Information in Multivalued Singly Connected Networks using Maximum Entropy Dawn E. Holmes	Maximum Entropy and Bayesian Methods Kluwer Academic, Dordrecht
	1999	The Efficient Estimation of Missing Information in Causal Inverted Multiway Trees Gerald R. Garside, Paul C. Rhodes, Dawn E. Holmes	Knowledge Based Systems Elsevier Sciences B.V. Vol. 12 pp 101-111
	2001	Independence Relationships Implied by D-separation in the Bayesian Model of a Causal Tree are Preserved by the Maximum Entropy Model Dawn E. Holmes	Bayesian Inference and Maximum Entropy Methods in Science and Engineering, AIP Conference Proceedings Melville New York
	2001	Using Maximum Entropy to Estimate Mission Information in Tree-Like Causal Networks Dawn E. Holmes, Gerald R. Garside, Paul C. Rhodes	Vol. 20. Uncertainty in Intelligent and Information Systems: Advances in Fuzzy Systems – Applications and Theory
	2002	Probabilistic Decision Support Systems with Maximum Entropy Dawn E. Holmes	Innovations in Decision Support Systems
	2002	Incorporating Knowledge Expressed as Inequality Constraints into Bayesian Network Based Intelligent Systems by Maximizing Entropy Dawn E. Holmes	Proceedings of Int'l Conf. on Artificial Intelligence (IC-AI'02): Las Vegas, USA
	2003	Extending Bayesian Networks to Decision Support Systems where the Prior Distribution is Incomplete: A Software Prototype Dawn E. Holmes and Rick Hou	Proceedings Int'l Conf. on Artificial Intelligence (IC-AI'03): Las Vegas, USA
	2003	d-separation and Partial Correlation in Bayesian Networks with Incomplete Information. Dawn E. Holmes and Samuel Frame	Proceedings Int'l Conf. on Artificial Intelligence (IC-AI'03): Las Vegas, USA
	2004	Maximizing Entropy for Inference in a Class of Multiply Connected Networks Dawn E. Holmes	24th Conference on Maximum Entropy and Bayesian Methods. American Institute of Physics
	2005	An Improved Generalized Variable Elimination Algorithm in Bayesian Networks Dawn E. Holmes and Xiaofang Lei	Proceedings Int’l Conf. on Artificial Intelligence (IC-AI’05
	2005	Review of Clarke, G.M. and Cook, D. (2004) A Basic Course in Statistics Arnold. Dawn E. Holmes	Statistical Methods in Medical Research 2005; 14: p 525
	2005	Review of Daniel T.Larose (2005) "Discovering Knowledge in Data: An Introduction to Data Mining" Wiley, Dawn E. Holmes	Statistical Methods in Medical Research. 14. p 530-531. 2005
	2005	Review of “Practical Statistics for Nursing and Health Care”. J.Fowler et al. Wiley. Dawn E. Holmes	Statistical Methods in Medical Research Vol. 14 p 433-434
	2005	Optimizing Inequality Constrained Priors in Bayesian Networks Dawn E. Holmes	Bayesian Inference and Maximum Entropy Methods in Science and Engineering. American Institute of Physics 25th Conference Proceedings American Institute of Physics
	2006	Innovations in Machine Learning: Theory and Applications. Series: Studies in Fuzziness and Soft Computing Dawn E. Holmes and Lakhmi C. Jain (Eds)	Vol. 194. Springer
	2006	Toward a Generalized Bayesian Network Dawn E. Holmes	Bayesian Inference and Maximum Entropy Methods in Science and Engineering. 26th Conference Proceedings American Institute of Physics
	2006	StatClass Dawn E. Holmes and Lubella A. Lenaburg	McGraw-Hill Learning Solutions ISBN- 13 978-0-07-339125-0
	2007	Review of ‘Applied Bayesian Modeling and Causal Inference from Incomplete Data Perspectives’ (Gelman A, Meng Xiao-Li Eds., 2004) Wiley Dawn E. Holmes	Statistical Methods in Medical Research
	2008	The Reasoner: Volume 2, Number 3 March 2008 Editorial, Interview with Lakhmi C. Jain. Dawn E. Holmes	The Reasoner	Refereed Journal article
	2008	Innovations in Bayesian Networks. Theory and Applications. Studies in Computational Intelligence Dawn E Holmes and Lakhmi C. Jain (Eds)	Springer	Book
	2008	The Reasoner: Volume 2, Number 7 July 2008 Editorial , Interview with Richard Neapolitan Dawn E. Holme	The Reasoner	Refereed Journal article
	2008	Toward a Generalized Bayesian Network. (Re-printed in Innovations in Bayesian Networks with permission of the American Institute of Physics) Dawn E. Holmes	Springer	Refereed Book contribution
	2009	The Reasoner: Volume 3, Number 4 - April 2009 Editorial, Interview with Keith Devlin Dawn E. Holmes	The Reasoner	Refereed Journal article
	2010	George Boole, Boole’s Laws of Thought, Tautology, Recursion, Theorems, Resolution and Hilbert’s Programme. Key Terms in Logic Eds Jon Williamson and Federica Russo.	Continuum Press	Refereed Book contribution
	2010	The Efficacy of Intensive Statistical Programming Courses at the Undergraduate Level Dawn E. Holmes and Nicole Ifill. Joint Statistical Meetings.	American Statistical Association	Refereed Proceedings
	2010	Why Making Bayesian Networks Objectively Bayesian Make Sense Dawn E. Holmes. Causality in the Sciences. Eds. Phyllis McKay Illari, Federica Russo and Jon Williamson.	OUP	Refereed Book contribution
	2011	Data Mining: Foundations and Intelligent Paradigms. Volume 1: Clustering, Association and Classification Dawn E. Holmes and Lakhmi C. Jain (Eds) ***	Springer	Book
	2011	Data Mining Techniques in clustering, Association and Classification. Dawn E. Holmes, Jeffrey W. Tweedale and Lakhmi C. Jain. In Data Mining: Foundations and Intelligent Paradigms. Volume 1: Clustering, Association and Classification Dawn E. Holmes and Lakhmi C. Jain (Eds)	Springer	Refereed Book contribution
	2011	Advanced Modelling Paradigms in Data Mining. Dawn E. Holmes, Jeffrey W. Tweedale and Lakhmi C. Jain. In Data Mining: Foundations and Intelligent Paradigms. Volume 2: Statistical, Bayesian, Time Series and other Theoretical Aspects Dawn E. Holmes and Lakhmi C. Jain (Eds)	Springer	Book
	2011	Advances in Intelligent Data Mining Dawn E. Holmes, Jeffrey W. Tweedale and Lakhmi C. Jain. In Data Mining: Foundations and Intelligent Paradigms. Volume 3: Medical, Health, Social, Biological and Other Applications Dawn E. Holmes and Lakhmi C. Jain (Eds)	Springer	Refereed Book contribution
	2012	Data Mining: Foundations and Intelligent Paradigms. Volume 2: Statistical, Bayesian, Time Series and other Theoretical Aspects Dawn E. Holmes and Lakhmi C. Jain (Eds)	Springer	Book
	2012	Data Mining: Foundations and Intelligent Paradigms. Volume 3: Medical, Health, Social, Biological and Other Applications Dawn E. Holmes and Lakhmi C. Jain (Eds)	Springer	Refereed Book contribution

Books

		Data Mining: Foundations and Intelligent Paradigms: Volume 1 Clustering, Association and Classification	Data Mining: Foundations and Intelligent Paradigms: Volume 2 Statistical, Bayesian, Time Series and other Theoretical Aspects
Data Mining: Foundations and Intelligent Paradigms: Volume 3 Medical, Health, Social, Biological and other Applications	Textbook for PSTAT 5A