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Shi, T., Steyn, D. & Welch, W.J., 2014. Air Quality Model Evaluation Using Gaussian Process Modelling and Empirical Orthogonal Function Decomposition. In Air Pollution Modeling and its Application XXIII. Air Pollution Modeling and its Application XXIII. Springer International Publishing, pp. 457–462. Available at: http://link.springer.com/chapter/10.1007/978-3-319-04379-1_75.
Campbell, T. & How, J.P., 2014. Approximate decentralized Bayesian inference. In Uncertainty in Artificial Intelligence. Uncertainty in Artificial Intelligence.
Maydeu-Olivares, A. & Joe, H., 2014. Assessing approximate fit in categorical data analysis. Multivariate Behavioral Research, 49, pp.305-328.
Evans, C. et al., 2014. Association between beta-interferon exposure and hospital events in multiple sclerosis. Pharmacoepidemiology and Drug Safety, 23, pp.1213-1222.
Evans, C. et al., 2014. Association between beta-interferon exposure and hospital events in multiple sclerosis. Pharmacoepidemiology and drug safety, 23, pp.1213–1222.
Gustafson, P., 2014. Bayesian inference in partially identified models: Is the shape of the posterior distribution useful?. Electronic Journal of Statistics, 8, pp.476-496.
Xia, M. & Gustafson, P., 2014. Bayesian sensitivity analyses for hidden sub-populations in weighted sampling. Canadian Journal of Statistics, 42, pp.436–450.
Gustafson, P., 2014. Bayesian Statistical Methodology for Observational Health Sciences Data. Statistics in Action: A Canadian Outlook, p.163.
Koulis, T., Muthukumarana, S. & Briercliffe, C., 2014. A Bayesian stochastic model for batting performance evaluation in one-day cricket. Journal of Quantitative Analysis in Sports, 10, pp.1–13. Available at: http://www.degruyter.com/view/j/jqas.2014.10.issue-1/jqas-2013-0057/jqas-2013-0057.xml.
Shaddick, G. & Zidek, J.V., 2014. A case study in preferential sampling: Long term monitoring of air pollution in the UK. Spatial Statistics, 9, pp.51–65.
Gustafson, P. & McCandless, L., 2014. Commentary: Priors, Parameters, and Probability: A Bayesian Perspective on Sensitivity Analysis. Epidemiology, 25, pp.910–912.
Campbell, H. & Dean, C.B., 2014. The consequences of proportional hazards based model selection. Statistics in medicine, 33, pp.1042–1056.
Falasinnu, T. et al., 2014. A critical appraisal of risk models for predicting sexually transmitted infections. Sexually transmitted diseases, 41, pp.321–330.
Lublin, F.D. et al., 2014. Defining the clinical course of multiple sclerosis: The 2013 revisions. Neurology, 83, pp.278-286.
Joe, H., 2014. Dependence Modeling with Copulas, Boca Raton, FL: Chapman & Hall/CRC. Available at: http://www.crcpress.com/product/isbn/9781466583221.
Falasinnu, T. et al., 2014. Deriving and validating a risk estimation tool for screening asymptomatic chlamydia and gonorrhea. Sexually transmitted diseases, 41, pp.706–712.
Zhao, Y. et al., 2014. Detection of unusual increases in MRI lesion counts in individual multiple sclerosis patients. Journal of the American Statistical Association, 109, pp.119–132. Available at: http://amstat.tandfonline.com/doi/abs/10.1080/01621459.2013.847373.
Lindsten, F. et al., 2014. Divide-and-Conquer with Sequential Monte Carlo. arXiv, 1406.4993.