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Weekly informative reparametrisations for loatino-scale mixtures. Kamary, K., Lee, J. and Robert, C. P. Accepted JCGS.
Importance sampling schemes for evidence approximation in mixture models. Lee, J. and Robert, C. P. (2016). Bayesian Analysis. 11 (2). 573-597.
Threshold selection method using the measure of surprise. Lee, J., Fan, Y., and Sisson, S. (2015). Computational Statistics & Data Analysis. 85. 84-99.
Detecting de-lamination in composite beams using natural frequencies and the Bayesian inference. Chung, H. and Lee, J. (2015). The 22nd International Congress on Sound and Vibration. Florence, Italy.
Detecting defects in composite beams and plates using Bayesian inference. Chung, H. and Lee, J. (2014). International conference on noise and vibration engineering 2014, Leuven, Belgium.
Issues in designing hybrid algorithms. Lee, J., Robert, C. P., and Mengersen, K. L. (2013) In Case Studies in Bayesian Statistical Modelling and Analysis (eds C. L. Alston, K. L. Mengersen, A. N. Pettitt). Wiley Series in Probability and Statistics.
Population Monte Carlo algorithm in high dimensions. Lee, J., Mengersen, K. L., and McVinish, R., (2011) Methodology and Computing in Applied Probability,13, 2, 369-389.
Bayesian Inference on Mixtures of Distributions. Lee, K., Mengersen, K. L., Marin, J.-M., and Robert, C. P. (2008) In Perspectives in Mathematical Sciences. Stat. Sci. Interdiscip. Res., 7, 165-202. World Sci. Publ., Hackensack, NJ.
R-package, Ultimixt : Bayesian analysis of a non-informative parameterization for Gaussian mixture distributions. Kamary, K., and Lee, J.
Last updated: 01-Feb-2018 4.51pm
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