Research output per year
Research output per year
- 24 results
Search results
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2019
Predicting persistent depressive symptoms in older adults: a machine learning approach to personalised mental healthcare
Hatton, C., Paton, L. W., McMillan, D., Cussens, J., Gilbody, S. & Tiffin, P. A., 1 Mar 2019, In: Journal of affective disorders. 246, p. 857-860 4 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
2017
Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets and Complexity
Cussens, J., Järvisalo, M., Korhonen, J. H. & Bartlett, M., 27 Jan 2017, In: Journal of Artificial Intelligence Research (JAIR). 58, p. 185-229 45 p.Research output: Contribution to journal › Article › peer-review
Open Access -
Integrated testing strategies can be optimal for chemical risk classification.
Pitchford, J. W., Cussens, J., Raseta, M. & Doe, J., Aug 2017, In: Mathematical Biosciences. 290, p. 1-8 9 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Polyhedral aspects of score equivalence in Bayesian network structure learning
Cussens, J., Haws, D. & Studený, M., Jul 2017, In: Mathematical Programming. 164, 1, p. 285-324 40 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Towards using the chordal graph polytope in learning decomposable models
Studený, M. & Cussens, J., Sept 2017, In: International Journal of Approximate Reasoning. 88, p. 259-281 23 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
2016
Distributional logic programming for Bayesian knowledge representation
Angelopoulos, N. & Cussens, J., 17 Aug 2016, (E-pub ahead of print) In: International Journal of Approximate Reasoning. 80, p. 52-66 16 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
2015
Exact estimation of multiple directed acyclic graphs
Oates, C. J., Smith, J. Q., Mukherjee, S. & Cussens, J., 2015, In: Statistics and computing. p. 1-15 15 p.Research output: Contribution to journal › Article › peer-review
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Identification and quantification of heteroscedasticity in stock–recruitment relationships
Panikian, G., Cussens, J. & Pitchford, J. W., Aug 2015, In: Canadian Journal of Fisheries and Aquatic Sciences. 72, 8, p. 1259-1271 13 p.Research output: Contribution to journal › Article › peer-review
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Integer Linear Programming for the Bayesian network structure learning problem
Bartlett, M. & Cussens, J., 10 Mar 2015, (E-pub ahead of print) In: Artificial Intelligence. 244, p. 258-271 14 p.Research output: Contribution to journal › Article › peer-review
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Learning failure-free PRISM programs
Alsanie, W. & Cussens, J., Dec 2015, In: International Journal of Approximate Reasoning. 67, p. 73-110 38 p.Research output: Contribution to journal › Article › peer-review
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Searching Multiregression Dynamic Models of Resting-State fMRI Networks Using Integer Programming
Costa, L., Smith, J., Nichols, T., Cussens, J., Duff, E. P. & Makin, T. R., 1 Jun 2015, In: Bayesian Analysis. 10, 2, p. 441-478 38 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
2014
Improved Maximum Likelihood Reconstruction of Complex Multi-generational Pedigrees
Sheehan, N., Bartlett, M. & Cussens, J., Nov 2014, In: Theoretical Population Biology. 97, p. 11-19 10 p.Research output: Contribution to journal › Article › peer-review
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Integer Programming for Bayesian Network Structure Learning
Cussens, J., Mar 2014, In: Quality Technology and Quantitative Management. 11, 1, p. 99-110 12 p.Research output: Contribution to journal › Article › peer-review
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Probability, uncertainty and artificial intelligence
Cussens, J., Nov 2014, In: Metascience. 23, 3, p. 505-511 7 p.Research output: Contribution to journal › Article › peer-review
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2013
Maximum Likelihood Pedigree Reconstruction using Integer Linear Programming
Cussens, J., Bartlett, M., Jones, E. & Sheehan, N., Jan 2013, In: Genetic Edpidemiology. 37, 1, p. 69-83 15 p.Research output: Contribution to journal › Article › peer-review
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2012
Online Bayesian inference for the parameters of PRISM programs
Cussens, J., Dec 2012, In: Machine Learning. 89, 3, p. 279-297 19 p.Research output: Contribution to journal › Article › peer-review
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2008
Bayesian learning of Bayesian networks with informative priors
Angelopoulos, N. & Cussens, J., Nov 2008, In: Annals of Mathematics and Artificial Intelligence. 54, 1-3, p. 53-98 46 p.Research output: Contribution to journal › Article › peer-review
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2007
Bayesian classification and regression trees
Cussens, J., 2007, In: Expert Update. 9, 3, p. 37-42 6 p.Research output: Contribution to journal › Article › peer-review
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2004
Leibniz on Estimating the Uncertain: An English Translation of De incerti aestimatione with Commentary
Melo, W. D. C. D. & Cussens, J., 2004, In: Leibniz Review. 14, p. 31-53 23 p.Research output: Contribution to journal › Article › peer-review
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2001
Grammar learning using Inductive Logic Programming
Pulman, S. & Cussens, J., 1 May 2001, In: Oxford University Working Papers in Linguistics, Philology and Phonetics. 6, p. 31-45 15 p.Research output: Contribution to journal › Article
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Parameter estimation in stochastic logic programs
Cussens, J., 2001, In: Machine Learning. 44, 3, p. 245-271 27 p.Research output: Contribution to journal › Article › peer-review
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1999
Integrating Probabilistic and Logical Reasoning
Cussens, J., 1999, In: Electron. Trans. Artif. Intell.. 3, B, p. 79-103 25 p.Research output: Contribution to journal › Article › peer-review
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1996
Deduction, Induction and Probabilistic Support
Cussens, J., 1 Jul 1996, In: Synthese. 108, 1, p. 1-10 10 p.Research output: Contribution to journal › Article › peer-review
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Effective Sample Size in a Dichotomous Process with Noise
Cussens, J., 1996, In: Communications in Statistics: Theory and Methods. 25, 6, p. 1233-1246 14 p.Research output: Contribution to journal › Article › peer-review