Research InterestsBayesian 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. RhodesInternational 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. RhodesProceedings: 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. GarsideInternational 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. HolmesMaximum 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. HolmesKnowledge 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. HolmesBayesian 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. RhodesVol. 20. Uncertainty in Intelligent and Information Systems: Advances in Fuzzy Systems – Applications and Theory 2002 Probabilistic Decision Support Systems with Maximum Entropy

Dawn E. HolmesInnovations in Decision Support Systems 2002 Incorporating Knowledge Expressed as Inequality Constraints into Bayesian Network Based Intelligent Systems by Maximizing Entropy

Dawn E. HolmesProceedings 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 HouProceedings 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 FrameProceedings 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. Holmes24th 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 LeiProceedings 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. HolmesStatistical 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. HolmesStatistical 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. HolmesStatistical Methods in Medical Research Vol. 14 p 433-434 2005 Optimizing Inequality Constrained Priors in Bayesian Networks

Dawn E. HolmesBayesian 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. HolmesBayesian Inference and Maximum Entropy Methods in Science and Engineering. 26th Conference Proceedings American Institute of Physics 2006 StatClass

Dawn E. Holmes and Lubella A. LenaburgMcGraw-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. HolmesStatistical Methods in Medical Research 2008 The Reasoner: Volume 2, Number 3 March 2008

Editorial, Interview with Lakhmi C. Jain. Dawn E. HolmesThe 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. HolmeThe 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. HolmesSpringer Refereed Book contribution 2009 The Reasoner: Volume 3, Number 4 - April 2009

Editorial, Interview with Keith Devlin Dawn E. HolmesThe 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