
[1]

L. Allison, C.S. Wallace, and C.N. Yee.
Finitestate models in the alignment of macromolecules.
Journal of Molecular Evolution, 35:7789, 1992.
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[2]

D. M. Boulton and C. S. Wallace.
An information measure for singlelink classification.
The Computer Journal, 18(3):236238, August 1975.
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[3]

D.M. Boulton and C.S. Wallace.
The information content of a multistate distribution.
Journal of Theoretical Biology, 23:269278, 1969.
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[4]

D.M. Boulton and C.S. Wallace.
A program for numerical classification.
Computer Journal, 13:6369, 1970.
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[5]

D.M. Boulton and C.S. Wallace.
An information measure for hierarchic classification.
The Computer Journal, 16:254261, 1973.
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[6]

D.M. Boulton and C.S. Wallace.
A comparison between information measure classification.
In Proceedings of ANZAAS Congress, Perth, August 1973.
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[7]

D. L. Dowe, L. Allison, T. I. Dix, L. Hunter, C. S. Wallace, and T. Edgoose.
Circular clustering of protein dihedral angles by minimum message
length.
In Pacific Symposium on Biocomputing '96, pages 242255. World
Scientific, 1996.
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[8]

D. L. Dowe, J. J. Oliver, and C. S. Wallace.
MML estimation of the parameters of the spherical Fisher
distribution.
In A. Sharma et al., editor, Proc. 7th Conf. Algorithmic
Learning Theory (ALT'96), LNAI 1160, pages 213227, Sydney, Australia,
October 1996.
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[9]

D. L. Dowe and C. S. Wallace.
Resolving the NeymanScott problem by Minimum Message
Length.
In Proc. Computing Science and Statistics  28th Symposium on
the interface, volume 28, pages 614618, 1997.
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[10]

D.L. Dowe, R.A. Baxter, J.J. Oliver, and C.S. Wallace.
Point Estimation using the KullbackLeibler Loss Function
and MML.
In Proc. 2nd Pacific Asian Conference on Knowledge
Discovery and Data Mining (PAKDD'98), pages 8795, Melbourne,
Australia, April 1998. Springer Verlag.
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[11]

D.L. Dowe, J.J. Oliver, R.A. Baxter, and C.S. Wallace.
Bayesian estimation of the von Mises concentration parameter.
Technical report TR 236, Dept. of Computer Science, Monash
University, Clayton, Victoria 3168, Australia, 1995.
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[12]

D.L. Dowe, J.J. Oliver, R.A. Baxter, and C.S. Wallace.
Bayesian estimation of the von Mises concentration parameter.
In Proc. 15th Maximum Entropy Conference, Santa Fe, New Mexico,
August 1995.
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[13]

D.L. Dowe and C.S. Wallace.
Resolving the NeymanScott problem by Minimum Message
Length.
Technical report TR no. 97/307, Dept. of Computer Science, Monash
University, Clayton, Victoria 3168, Australia, February 1997.
Also in Proc. Sydney Int. Stat. Congr. (SISC96), Sydney, pages
197198; and in IMS Bulletin (1996), 25 (4), pp410411.
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[14]

D.L. Dowe and C.S. Wallace.
Kolmogorov Complexity, Minimum Message Length and Inverse
Learning.
In Proc. 14th Australian Statistical Conference (ASC14), page
144, Gold Coast, Qld., Australia, July 1998.
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[15]

G. E. Farr and C. S. Wallace.
The complexity of Strict Minimum Message Length inference.
The Computer Journal, 45(3):285292, 2002.
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[16]

M.P. Georgeff and C.S. Wallace.
A general objective for inductive inference.
Technical Report 32, Dept of Computer Science, Monash University,
Clayton, Victoria 3168, Australia, 1983.
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[17]

M.P. Georgeff and C.S. Wallace.
A general criterion for inductive inference.
In T. O'Shea, editor, Advances in Artificial Intelligence :
Proc. Sixth European Conference on Artificial Intelligence, pages 473482,
Amsterdam, 1984. North Holland.
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[18]

M.P. Georgeff and C.S. Wallace.
A general selection criterion for inductive inference.
Technical report TR 44, Dept. of Computer Science, Monash
University, Clayton, Victoria 3168, Australia, June 1984.
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[19]

M.P. Georgeff and C.S. Wallace.
Minimum information estimation of structure.
In T. O'Shea, editor, Advances in Artificial Intelligence,
pages 219228. Elsevier, 1985.
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[20]

K. B. Korb and C. S. Wallace.
In search of the philosopher's stone: Remarks on humphreys and
freedman's critique of causal discovery.
Brit. J. Phil. Sci., pages 543553, 1997.
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[21]

J. R. Neil, C. S. Wallace, and K. B. Korb.
Learning bayesian networks with restricted causal interactions.
In Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI99), Stockholm, Sweden, July 30August 1, 1999,
pages 486493, San Francisco, CA, USA, 1999. Morgan Kaufman Publishers.
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[22]

J.J. Oliver, D.L. Dowe, and C.S. Wallace.
Inferring decision graphs using the minimum message length principle.
In A. Adams and L. Sterling, editors, Proceedings of the 5th
Australian Joint Conference on Artificial Intelligence, pages 361367.
World Scientific, Singapore, 1992.
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[23]

J.D. Patrick and C.S. Wallace.
Stone circle geometries: an information theory approach.
In D.C. Heggie, editor, Archaeoastronomy in the Old World,
pages 231264. Cambridge University Press, Cambridge, 1982.
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[24]

Grace W. Rumantir and Chris S. Wallace.
Minimum Message Length criterion for secondorder polynomial
model selection applied to tropical cyclone intensity forecasting.
In Michael R. Berthold et al., editor, Advances in Intelligent
Data Analysis V: Fifth International Symposium on Intelligent Data Analysis,
IDA (2003), pages 486496. SpringerVerlag, 2003.
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[25]

M. Viswanathan and C. S. Wallace.
A note on the comparison of polynomial selection methods.
In Proc. 7th Int. Workshop on Artif. Intelligence and
Statistics, pages 169177. Morgan Kaufmann, 1999.
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[26]

M. Viswanathan, C.S. Wallace, D.L. Dowe, and K.B. Korb.
Finding cutpoints in noisy binary sequences  a revised empirical
evaluation.
In Proc. 12th Australian Joint Conf. on Artif. Intelligence,
Lecture Notes in Artificial Intelligence (LNAI), pages 405416. ???, 1999.
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[27]

C. S. Wallace.
Multiple factor analysis by MML estimation.
Technical Report 95/218, Dept. of Computer Science, Monash
University, Clayton, Victoria 3168, Australia, 1995.
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[28]

C. S. Wallace.
Statistical and Inductive Inference by Minimum Message
Length.
Springer, Berlin, Germany, 2005.
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[29]

C. S. Wallace and D. M. Boulton.
An information measure for classification.
Computer Journal, 11:185194, 1968.
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[30]

C. S. Wallace and D. M. Boulton.
An invariant Bayes method for point estimation.
Classification Society Bulletin, 3(3):1134, 1975.
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[31]

C. S. Wallace and D. L. Dowe.
MML estimation of the von Mises concentration parameter.
Tech Rept TR 93/193, Dept. of Comp. Sci., Monash Univ., Clayton
3168, Australia, 1993.
prov. accepted, Aust. and N.Z. J. Stat.
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[32]

C. S. Wallace and D. L. Dowe.
Refinements of MDL and MML coding.
Computer Journal, 42(4):330337, 1999.
Special issue on Kolmogorov Complexity.
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[33]

C. S. Wallace and D. L. Dowe.
MML clustering of multistate, Poisson, von Mises circular and
Gaussian distributions.
Journal of Statistics and Computing, 10(1):7383, January
2000.
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[34]

C. S. Wallace and P. R. Freeman.
Estimation and inference by compact coding.
J. Royal Statistical Society B, 49:240252, 1987.
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[35]

C. S. Wallace and P. R. Freeman.
Single factor analysis by MML estimation.
Journal of the Royal Statistical Society (Series B),
54:195209, 1992.
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[36]

Chris S. Wallace.
On the selection of the order of a polynomial model.
Technical report, Royal Holloway College, 1997.
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[37]

Chris S. Wallace and David M. Boulton.
A information measure for classification.
The Computer Journal, 11(2):185194, 1968.
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[38]

Chris S. Wallace and David L. Dowe.
Minimum message length and Kolmogorov complexity.
Computer Journal, 42(4):270283, 1999.
Special issue on Kolmogorov complexity.
http://www3.oup.co.uk/computer_journal/hdb/Volume_42/Issue_04/.
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[39]

C.S. Wallace.
Estimation and inference by compact coding.
Technical report TR 46, Dept. of Computer Science, Monash
University, Clayton, Victoria 3168, Australia, August 1984.
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[40]

C.S. Wallace.
An improved program for classification.
In Proceedings of the Nineteenth Australian Computer Science
Conference (ACSC9), volume 8, pages 357366, Monash University, Australia,
1986.
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[41]

C.S. Wallace.
Classification by Minimum Message Length inference.
In G. Goos and J. Hartmanis, editors, Advances in Computing and
Information  ICCI '90, pages 7281. SpringerVerlag, Berlin, 1990.
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[42]

C.S. Wallace.
False Oracles and SMML Estimators.
In Proc. Information, Statistics and Induction in Science
conference (ISIS'96), pages 304316, Singapore, 1996. World Scientific.
Was Tech Rept TR 89/128, Monash University, Australia, 1989.
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[43]

C.S. Wallace.
False Oracles and SMML Estimators.
In D.L. Dowe, K.B. Korb, and J.J. Oliver, editors, Proceedings
of the Information, Statistics and Induction in Science (ISIS) Conference,
pages 304316, Melbourne, Australia, August 1996. World Scientific.
Was Tech Rept 89/128, Dept. Comp. Sci., Monash Univ., Australia, June
1989.
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[44]

C.S. Wallace.
Intrinsic Classification of SpatiallyCorrelated Data.
Computer Journal, 41(8):602611, 1998.
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[45]

C.S. Wallace and D.L. Dowe.
Estimation of the von Mises concentration parameter using Minimum
Message Length.
In Proc. 12th Australian Statistical Soc. Conf., Monash
University, Australia, 1994.
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[46]

C.S. Wallace and D.L. Dowe.
Intrinsic classification by MML  the Snob program.
In C. Zhang and et al., editors, Proc. 7th Australian Joint
Conf. on Artif. Intelligence, pages 3744. World Scientific, Singapore,
1994.
See
http://www.csse.monash.edu.au/0.6ex~dld/Snob.html.
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[47]

C.S. Wallace and D.L. Dowe.
MML mixture modelling of Multistate, Poisson, von Mises
circular and Gaussian distributions.
In Proc. Sydney International Statistical Congress (SISC96),
page 197, Sydney, Australia, 1996.
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[48]

C.S. Wallace and D.L. Dowe.
MML mixture modelling of Multistate, Poisson, von Mises
circular and Gaussian distributions.
In Proc. Computing Science and Statistics  28th Symposium on
the interface, volume 28, pages 608613, 1997.
[ bib ]

[49]

C.S. Wallace and D.L. Dowe.
MML mixture modelling of Multistate, Poisson, von Mises
circular and Gaussian distributions.
In Proc. 6th Int. Workshop on Artif. Intelligence and
Statistics, pages 529536, 1997.
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[50]

C.S. Wallace and D.L. Dowe.
Minimum Message Length and Kolmogorov Complexity.
Computer Journal, 42(4):270283, 1999.
Special issue on Kolmogorov Complexity.
[ bib ]

[51]

C.S. Wallace and D.L. Dowe.
Rejoinder.
Computer Journal, 42(4):345347, 1999.
Special issue on Kolmogorov Complexity.
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[52]

C.S. Wallace, K.B. Korb, and H. Dai.
Causal discovery via mml.
Technical report 254, Dept. of Computer Science, Monash University,
Clayton, Victoria 3168, Australia, 1996.
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[53]

C.S. Wallace and J.D. Patrick.
Coding decision trees.
Machine Learning, 11:722, 1993.
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